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mCODE: Improving data sharing to enhance cancer care
An initiative designed to improve sharing of patient data may provide “tremendous benefits” in cancer care and research, according to authors of a review article.
The goals of the initiative, called Minimal Common Oncology Data Elements (mCODE), were to identify the data elements in electronic health records that are “essential” for making treatment decisions and create “a standardized computable data format” that would improve the exchange of data across EHRs, according to the mCODE website.
Travis J. Osterman, DO, of Vanderbilt University Medical Center in Nashville, Tenn., and colleagues described the mCODE initiative in a review published in JCO Clinical Cancer Informatics.
At present, commercially available EHRs are poorly designed to support modern oncology workflow, requiring laborious data entry and lacking a common library of oncology-specific discrete data elements. As an example, most EHRs poorly support the needs of precision oncology and clinical genetics, since next-generation sequencing and genetic test results are almost universally reported in PDF files.
In addition, basic, operational oncology data (e.g., cancer staging, adverse event documentation, response to treatment, etc.) are captured in EHRs primarily as an unstructured narrative.
Computable, analytical data are found for only the small percentage of patients in clinical trials. Even then, some degree of manual data abstraction is regularly required.
Interoperability of EHRs between practices and health care institutions is often so poor that the transfer of basic cancer-related information as analyzable data is difficult or even impossible.
Making progress: The 21st Century Cures Act
The American Society of Clinical Oncology has a more than 15-year history of developing oncology data standards. Unfortunately, progress in implementing these standards has been glacially slow. Impediments have included:
- A lack of conformance with clinical workflows.
- Failure to test standards on specific-use cases during pilot testing.
- A focus on data exchange, rather than the practical impediments to data entry.
- Poor engagement with EHR vendors in distributing clinical information modules with an oncology-specific focus
- Instability of data interoperability technologies.
The 21st Century Cures Act, which became law in December 2016, mandated improvement in the interoperability of health information through the development of data standards and application programming interfaces.
In early 2020, final rules for implementation required technology vendors to employ application programming interfaces using a single interoperability resource. In addition, payers were required to use the United States Core Data for Interoperability Standard for data exchange. These requirements were intended to provide patients with access to their own health care data “without special effort.”
As a fortunate byproduct, since EHR vendors are required to implement application program interfaces using the Health Level Seven International (HL7) Fast Healthcare Interoperability Resource (FHIR) Specification, the final rules could enable systems like mCODE to be more easily integrated with existing EHRs.
Lessons from CancerLinQ
ASCO created the health technology platform CancerLinQ in 2014, envisioning that it could become an oncology-focused learning health system – a system in which internal data and experience are systematically integrated with external evidence, allowing knowledge to be put into practice.
CancerLinQ extracts data from EHRs and other sources via direct software connections. CancerLinQ then aggregates, harmonizes, and normalizes the data in a cloud-based environment.
The data are available to participating practices for quality improvement in patient care and secondary research. In 2020, records of cancer patients in the CancerLinQ database surpassed 2 million.
CancerLinQ has been successful. However, because of the nature of the EHR ecosystem and the scope and variability of data capture by clinicians, supporting a true learning health system has proven to be a formidable task. Postprocessing manual review using trained human curators is laborious and unsustainable.
The CancerLinQ experience illustrated that basic cancer-pertinent data should be standardized in the EHR and collected prospectively.
The mCODE model
The mCODE initiative seeks to facilitate progress in care quality, clinical research, and health care policy by developing and maintaining a standard, computable, interoperable data format.
Guiding principles that were adopted early in mCODE’s development included:
- A collaborative, noncommercial, use case–driven developmental model.
- Iterative processes.
- User-driven development, refinement, and maintenance.
- Low ongoing maintenance requirements.
A foundational moment in mCODE’s development involved achieving consensus among stakeholders that the project would fail if EHR vendors required additional data entry by users.
After pilot work, a real-world endpoints project, working-group deliberation, public comment, and refinement, the final data standard included six primary domains: patient, disease, laboratory data/vital signs, genomics, treatment, and outcome.
Each domain is further divided into several concepts with specific associated data elements. The data elements are modeled into value sets that specify the possible values for the data element.
To test mCODE, eight organizations representing oncology EHR vendors, standards developers, and research organizations participated in a cancer interoperability track. The comments helped refine mCODE version 1.0, which was released in March 2020 and is accessible via the mCODE website.
Additions will likely be reviewed by a technical review group after external piloting of new use cases.
Innovation, not regulation
Every interaction between a patient and care provider yields information that could lead to improved safety and better outcomes. To be successful, the information must be collected in a computable format so it can be aggregated with data from other patients, analyzed without manual curation, and shared through interoperable systems. Those data should also be secure enough to protect the privacy of individual patients.
mCODE is a consensus data standard for oncology that provides an infrastructure to share patient data between oncology practices and health care systems while promising little to no additional data entry on the part of clinicians. Adoption by sites will be critical, however.
Publishing the standard through the HL7 FHIR technology demonstrated to EHR vendors and regulatory agencies the stability of HL7, an essential requirement for its incorporation into software.
EHR vendors and others are engaged in the CodeX HL7 FHIR Accelerator to design projects to expand and/or modify mCODE. Their creativity and innovativeness via the external advisory mCODE council and/or CodeX will be encouraged to help mCODE reach its full potential.
As part of CodeX, the Community of Practice, an open forum for end users, was established to provide regular updates about mCODE-related initiatives and use cases to solicit in-progress input, according to Robert S. Miller, MD, medical director of CancerLinQ and an author of the mCODE review.
For mCODE to be embraced by all stakeholders, there should be no additional regulations. By engaging stakeholders in an enterprise that supports innovation and collaboration – without additional regulation – mCODE could maximize the potential of EHRs that, until now, have assisted us only marginally in accomplishing those goals.
mCODE is a joint venture of ASCO/CancerLinQ, the Alliance for Clinical Trials in Oncology Foundation, the MITRE Corporation, the American Society for Radiation Oncology, and the Society of Surgical Oncology.
Dr. Osterman disclosed a grant from the National Cancer Institute and relationships with Infostratix, eHealth, AstraZeneca, Outcomes Insights, Biodesix, MD Outlook, GenomOncology, Cota Healthcare, GE Healthcare, and Microsoft. Dr. Miller and the third review author disclosed no conflicts of interest.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
An initiative designed to improve sharing of patient data may provide “tremendous benefits” in cancer care and research, according to authors of a review article.
The goals of the initiative, called Minimal Common Oncology Data Elements (mCODE), were to identify the data elements in electronic health records that are “essential” for making treatment decisions and create “a standardized computable data format” that would improve the exchange of data across EHRs, according to the mCODE website.
Travis J. Osterman, DO, of Vanderbilt University Medical Center in Nashville, Tenn., and colleagues described the mCODE initiative in a review published in JCO Clinical Cancer Informatics.
At present, commercially available EHRs are poorly designed to support modern oncology workflow, requiring laborious data entry and lacking a common library of oncology-specific discrete data elements. As an example, most EHRs poorly support the needs of precision oncology and clinical genetics, since next-generation sequencing and genetic test results are almost universally reported in PDF files.
In addition, basic, operational oncology data (e.g., cancer staging, adverse event documentation, response to treatment, etc.) are captured in EHRs primarily as an unstructured narrative.
Computable, analytical data are found for only the small percentage of patients in clinical trials. Even then, some degree of manual data abstraction is regularly required.
Interoperability of EHRs between practices and health care institutions is often so poor that the transfer of basic cancer-related information as analyzable data is difficult or even impossible.
Making progress: The 21st Century Cures Act
The American Society of Clinical Oncology has a more than 15-year history of developing oncology data standards. Unfortunately, progress in implementing these standards has been glacially slow. Impediments have included:
- A lack of conformance with clinical workflows.
- Failure to test standards on specific-use cases during pilot testing.
- A focus on data exchange, rather than the practical impediments to data entry.
- Poor engagement with EHR vendors in distributing clinical information modules with an oncology-specific focus
- Instability of data interoperability technologies.
The 21st Century Cures Act, which became law in December 2016, mandated improvement in the interoperability of health information through the development of data standards and application programming interfaces.
In early 2020, final rules for implementation required technology vendors to employ application programming interfaces using a single interoperability resource. In addition, payers were required to use the United States Core Data for Interoperability Standard for data exchange. These requirements were intended to provide patients with access to their own health care data “without special effort.”
As a fortunate byproduct, since EHR vendors are required to implement application program interfaces using the Health Level Seven International (HL7) Fast Healthcare Interoperability Resource (FHIR) Specification, the final rules could enable systems like mCODE to be more easily integrated with existing EHRs.
Lessons from CancerLinQ
ASCO created the health technology platform CancerLinQ in 2014, envisioning that it could become an oncology-focused learning health system – a system in which internal data and experience are systematically integrated with external evidence, allowing knowledge to be put into practice.
CancerLinQ extracts data from EHRs and other sources via direct software connections. CancerLinQ then aggregates, harmonizes, and normalizes the data in a cloud-based environment.
The data are available to participating practices for quality improvement in patient care and secondary research. In 2020, records of cancer patients in the CancerLinQ database surpassed 2 million.
CancerLinQ has been successful. However, because of the nature of the EHR ecosystem and the scope and variability of data capture by clinicians, supporting a true learning health system has proven to be a formidable task. Postprocessing manual review using trained human curators is laborious and unsustainable.
The CancerLinQ experience illustrated that basic cancer-pertinent data should be standardized in the EHR and collected prospectively.
The mCODE model
The mCODE initiative seeks to facilitate progress in care quality, clinical research, and health care policy by developing and maintaining a standard, computable, interoperable data format.
Guiding principles that were adopted early in mCODE’s development included:
- A collaborative, noncommercial, use case–driven developmental model.
- Iterative processes.
- User-driven development, refinement, and maintenance.
- Low ongoing maintenance requirements.
A foundational moment in mCODE’s development involved achieving consensus among stakeholders that the project would fail if EHR vendors required additional data entry by users.
After pilot work, a real-world endpoints project, working-group deliberation, public comment, and refinement, the final data standard included six primary domains: patient, disease, laboratory data/vital signs, genomics, treatment, and outcome.
Each domain is further divided into several concepts with specific associated data elements. The data elements are modeled into value sets that specify the possible values for the data element.
To test mCODE, eight organizations representing oncology EHR vendors, standards developers, and research organizations participated in a cancer interoperability track. The comments helped refine mCODE version 1.0, which was released in March 2020 and is accessible via the mCODE website.
Additions will likely be reviewed by a technical review group after external piloting of new use cases.
Innovation, not regulation
Every interaction between a patient and care provider yields information that could lead to improved safety and better outcomes. To be successful, the information must be collected in a computable format so it can be aggregated with data from other patients, analyzed without manual curation, and shared through interoperable systems. Those data should also be secure enough to protect the privacy of individual patients.
mCODE is a consensus data standard for oncology that provides an infrastructure to share patient data between oncology practices and health care systems while promising little to no additional data entry on the part of clinicians. Adoption by sites will be critical, however.
Publishing the standard through the HL7 FHIR technology demonstrated to EHR vendors and regulatory agencies the stability of HL7, an essential requirement for its incorporation into software.
EHR vendors and others are engaged in the CodeX HL7 FHIR Accelerator to design projects to expand and/or modify mCODE. Their creativity and innovativeness via the external advisory mCODE council and/or CodeX will be encouraged to help mCODE reach its full potential.
As part of CodeX, the Community of Practice, an open forum for end users, was established to provide regular updates about mCODE-related initiatives and use cases to solicit in-progress input, according to Robert S. Miller, MD, medical director of CancerLinQ and an author of the mCODE review.
For mCODE to be embraced by all stakeholders, there should be no additional regulations. By engaging stakeholders in an enterprise that supports innovation and collaboration – without additional regulation – mCODE could maximize the potential of EHRs that, until now, have assisted us only marginally in accomplishing those goals.
mCODE is a joint venture of ASCO/CancerLinQ, the Alliance for Clinical Trials in Oncology Foundation, the MITRE Corporation, the American Society for Radiation Oncology, and the Society of Surgical Oncology.
Dr. Osterman disclosed a grant from the National Cancer Institute and relationships with Infostratix, eHealth, AstraZeneca, Outcomes Insights, Biodesix, MD Outlook, GenomOncology, Cota Healthcare, GE Healthcare, and Microsoft. Dr. Miller and the third review author disclosed no conflicts of interest.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
An initiative designed to improve sharing of patient data may provide “tremendous benefits” in cancer care and research, according to authors of a review article.
The goals of the initiative, called Minimal Common Oncology Data Elements (mCODE), were to identify the data elements in electronic health records that are “essential” for making treatment decisions and create “a standardized computable data format” that would improve the exchange of data across EHRs, according to the mCODE website.
Travis J. Osterman, DO, of Vanderbilt University Medical Center in Nashville, Tenn., and colleagues described the mCODE initiative in a review published in JCO Clinical Cancer Informatics.
At present, commercially available EHRs are poorly designed to support modern oncology workflow, requiring laborious data entry and lacking a common library of oncology-specific discrete data elements. As an example, most EHRs poorly support the needs of precision oncology and clinical genetics, since next-generation sequencing and genetic test results are almost universally reported in PDF files.
In addition, basic, operational oncology data (e.g., cancer staging, adverse event documentation, response to treatment, etc.) are captured in EHRs primarily as an unstructured narrative.
Computable, analytical data are found for only the small percentage of patients in clinical trials. Even then, some degree of manual data abstraction is regularly required.
Interoperability of EHRs between practices and health care institutions is often so poor that the transfer of basic cancer-related information as analyzable data is difficult or even impossible.
Making progress: The 21st Century Cures Act
The American Society of Clinical Oncology has a more than 15-year history of developing oncology data standards. Unfortunately, progress in implementing these standards has been glacially slow. Impediments have included:
- A lack of conformance with clinical workflows.
- Failure to test standards on specific-use cases during pilot testing.
- A focus on data exchange, rather than the practical impediments to data entry.
- Poor engagement with EHR vendors in distributing clinical information modules with an oncology-specific focus
- Instability of data interoperability technologies.
The 21st Century Cures Act, which became law in December 2016, mandated improvement in the interoperability of health information through the development of data standards and application programming interfaces.
In early 2020, final rules for implementation required technology vendors to employ application programming interfaces using a single interoperability resource. In addition, payers were required to use the United States Core Data for Interoperability Standard for data exchange. These requirements were intended to provide patients with access to their own health care data “without special effort.”
As a fortunate byproduct, since EHR vendors are required to implement application program interfaces using the Health Level Seven International (HL7) Fast Healthcare Interoperability Resource (FHIR) Specification, the final rules could enable systems like mCODE to be more easily integrated with existing EHRs.
Lessons from CancerLinQ
ASCO created the health technology platform CancerLinQ in 2014, envisioning that it could become an oncology-focused learning health system – a system in which internal data and experience are systematically integrated with external evidence, allowing knowledge to be put into practice.
CancerLinQ extracts data from EHRs and other sources via direct software connections. CancerLinQ then aggregates, harmonizes, and normalizes the data in a cloud-based environment.
The data are available to participating practices for quality improvement in patient care and secondary research. In 2020, records of cancer patients in the CancerLinQ database surpassed 2 million.
CancerLinQ has been successful. However, because of the nature of the EHR ecosystem and the scope and variability of data capture by clinicians, supporting a true learning health system has proven to be a formidable task. Postprocessing manual review using trained human curators is laborious and unsustainable.
The CancerLinQ experience illustrated that basic cancer-pertinent data should be standardized in the EHR and collected prospectively.
The mCODE model
The mCODE initiative seeks to facilitate progress in care quality, clinical research, and health care policy by developing and maintaining a standard, computable, interoperable data format.
Guiding principles that were adopted early in mCODE’s development included:
- A collaborative, noncommercial, use case–driven developmental model.
- Iterative processes.
- User-driven development, refinement, and maintenance.
- Low ongoing maintenance requirements.
A foundational moment in mCODE’s development involved achieving consensus among stakeholders that the project would fail if EHR vendors required additional data entry by users.
After pilot work, a real-world endpoints project, working-group deliberation, public comment, and refinement, the final data standard included six primary domains: patient, disease, laboratory data/vital signs, genomics, treatment, and outcome.
Each domain is further divided into several concepts with specific associated data elements. The data elements are modeled into value sets that specify the possible values for the data element.
To test mCODE, eight organizations representing oncology EHR vendors, standards developers, and research organizations participated in a cancer interoperability track. The comments helped refine mCODE version 1.0, which was released in March 2020 and is accessible via the mCODE website.
Additions will likely be reviewed by a technical review group after external piloting of new use cases.
Innovation, not regulation
Every interaction between a patient and care provider yields information that could lead to improved safety and better outcomes. To be successful, the information must be collected in a computable format so it can be aggregated with data from other patients, analyzed without manual curation, and shared through interoperable systems. Those data should also be secure enough to protect the privacy of individual patients.
mCODE is a consensus data standard for oncology that provides an infrastructure to share patient data between oncology practices and health care systems while promising little to no additional data entry on the part of clinicians. Adoption by sites will be critical, however.
Publishing the standard through the HL7 FHIR technology demonstrated to EHR vendors and regulatory agencies the stability of HL7, an essential requirement for its incorporation into software.
EHR vendors and others are engaged in the CodeX HL7 FHIR Accelerator to design projects to expand and/or modify mCODE. Their creativity and innovativeness via the external advisory mCODE council and/or CodeX will be encouraged to help mCODE reach its full potential.
As part of CodeX, the Community of Practice, an open forum for end users, was established to provide regular updates about mCODE-related initiatives and use cases to solicit in-progress input, according to Robert S. Miller, MD, medical director of CancerLinQ and an author of the mCODE review.
For mCODE to be embraced by all stakeholders, there should be no additional regulations. By engaging stakeholders in an enterprise that supports innovation and collaboration – without additional regulation – mCODE could maximize the potential of EHRs that, until now, have assisted us only marginally in accomplishing those goals.
mCODE is a joint venture of ASCO/CancerLinQ, the Alliance for Clinical Trials in Oncology Foundation, the MITRE Corporation, the American Society for Radiation Oncology, and the Society of Surgical Oncology.
Dr. Osterman disclosed a grant from the National Cancer Institute and relationships with Infostratix, eHealth, AstraZeneca, Outcomes Insights, Biodesix, MD Outlook, GenomOncology, Cota Healthcare, GE Healthcare, and Microsoft. Dr. Miller and the third review author disclosed no conflicts of interest.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
FROM JCO CLINICAL CANCER INFORMATICS
USPSTF expands criteria for lung cancer screening
“This is great news because it means that nearly twice as many people are eligible to be screened, which we hope will allow clinicians to save more lives and help people remain healthy longer,” commented John Wong, MD, chief science officer, vice chair for clinical affairs, and chief of the Division of Clinical Decision Making at USPSTF.
The updated final recommendations were published online on March 9 in JAMA.
The USPSTF recommends annual screening with low-dose CT for adults aged 50-80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.
This updates guidance issued in 2013, which recommended annual screening for lung cancer for adults aged 55-80 years who had a 30 pack-year smoking history and who were either current smokers or had quit within the past 15 years.
The move will nearly double the number of people are now eligible for screening, up to 14.5 million individuals – an increase of 81% (6.4 million adults) from the 2013 recommendations.
The expanded criteria may help increase screening among Black individuals and women. Data show that both groups tend to smoke fewer cigarettes than White men and that Black persons are at higher risk for lung cancer than White persons. In addition, research has shown that about one-third of Black patients with lung cancer were diagnosed before the age of 55 years, which means they would not have been recommended for screening under the previous guidelines.
Uptake has been limited
To date, uptake of lung cancer screening has been very limited, from 6% to 18% of individuals who meet the eligibility criteria.
The new recommendations will open up screening to many more people, but challenges to implementation remain.
“The science is clear that lung cancer screening has the potential to save lives,” Dr. Wong told this news organization. “We recognize that there are existing barriers to screening everyone who is eligible, but clinicians and patients both deserve to know that screening can detect lung cancer early, when treatment has the best chance of being beneficial.”
He added that the hope is that these recommendations will encourage clinicians to examine the barriers to effective lung cancer screening in their communities and to do what they can to improve implementation. “We also hope to encourage patients to have conversations with their clinicians about whether they are eligible for screening and to discuss smoking cessation treatments if they are still smoking,” Dr. Wong added.
In an accompanying editorial, Louise M. Henderson, PhD, M. Patricia Rivera, MD, FCCP, and Ethan Basch, MD, all from the University of North Carolina at Chapel Hill, address some of the current challenges in implementation.
They note that reimbursement for lung cancer screening by Medicare requires submission of data to a Centers for Medicare & Medicaid Services–approved registry, and this can present problems for facilities serving less affluent communities or that have limited resources.
Medicaid coverage is also uneven. As of September 2020, lung cancer screening was covered by 38 Medicaid programs, but not by 9. For three programs, data on coverage were not available.
“With the new recommendations lowering the screening-eligible age to 50 years, many eligible individuals who are uninsured or who are receiving Medicaid and living in states that do not cover screening will have financial barriers to undergo screening,” they write.
In addition, many individuals in at-risk populations lack adequate geographic access to comprehensive lung cancer screening programs.
Expanding eligibility criteria is important, the editorialists point out, but barriers to screening, which include lack of insurance coverage and limited physical access to high-quality screening programs, highlight the complex problems with implementation that need to be addressed.
“A concerted effort to increase the reach of lung cancer screening is needed,” they write. “The 2021 USPSTF recommendation statement represents a leap forward in evidence and offers promise to prevent more cancer deaths and address screening disparities. But the greatest work lies ahead to ensure this promise is actualized.”
Advocacy needed
When approached for comment, Jianjun Zhang, MD, PhD, from the department of thoracic/head and neck medical oncology, University of Texas MD Anderson Cancer Center, Houston, said he supports the new guidelines, and they will lower mortality. “The data are pretty strong overall,” he said in an interview.
Although the uptake of screening is currently very low, he pointed out that, even if uptake remains the same, more lives will be saved because eligibility has been expanded. “More people will be getting screened, so it’s a start,” he said.
Aside from factors such as insurance and access, another problem involves primary care. “Time is very limited in primary care,” he said. “You have about 15 minutes, and it can be really hard to fit everything into a visit. Screening may get left out or may only get a brief mention.”
Advocacy is needed, Dr. Zhang pointed out. “Breast cancer has strong voices and advocacy, and people are more aware of mammography,” he said. “The information is disseminated out into the community. We need the same for lung cancer.”
Dr. Zhang emphasized that, even with the expanded criteria, many individuals will still be missed. “There are other risk factors besides smoking,” he said. “About 10% of lung cancers occur in never-smokers.”
Other risk factors include a family history of lung cancer, exposure to certain materials and chemicals, working in the mining industry, and genetics.
“We will move on to more personalized screening at some point,” he said. “But right now, we can’t make it too complicated for patients and doctors. We need to concentrate on increasing screening rates within these current criteria.”
The updated guidelines have been given a B recommendation, meaning the USPSTF recommends that clinicians provide the service to eligible patients, there is at least fair evidence that this service improves important health outcomes, and benefits outweigh harms.
The USPSTF is an independent, voluntary body. The U.S. Congress mandates that the Agency for Healthcare Research and Quality support the operations of the USPSTF. All members of the USPSTF receive travel reimbursement and an honorarium for participating in USPSTF meetings. The original article lists relevant financial relationships of task force members. Dr. Zhang has received grants from Johnson & Johnson and Merck, and adversary/consulting/honoraria fees from AstraZeneca, Bristol-Myers Squibb, GenePlus, Innovent, OrigMed, and Roche.
A version of this article first appeared on Medscape.com.
“This is great news because it means that nearly twice as many people are eligible to be screened, which we hope will allow clinicians to save more lives and help people remain healthy longer,” commented John Wong, MD, chief science officer, vice chair for clinical affairs, and chief of the Division of Clinical Decision Making at USPSTF.
The updated final recommendations were published online on March 9 in JAMA.
The USPSTF recommends annual screening with low-dose CT for adults aged 50-80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.
This updates guidance issued in 2013, which recommended annual screening for lung cancer for adults aged 55-80 years who had a 30 pack-year smoking history and who were either current smokers or had quit within the past 15 years.
The move will nearly double the number of people are now eligible for screening, up to 14.5 million individuals – an increase of 81% (6.4 million adults) from the 2013 recommendations.
The expanded criteria may help increase screening among Black individuals and women. Data show that both groups tend to smoke fewer cigarettes than White men and that Black persons are at higher risk for lung cancer than White persons. In addition, research has shown that about one-third of Black patients with lung cancer were diagnosed before the age of 55 years, which means they would not have been recommended for screening under the previous guidelines.
Uptake has been limited
To date, uptake of lung cancer screening has been very limited, from 6% to 18% of individuals who meet the eligibility criteria.
The new recommendations will open up screening to many more people, but challenges to implementation remain.
“The science is clear that lung cancer screening has the potential to save lives,” Dr. Wong told this news organization. “We recognize that there are existing barriers to screening everyone who is eligible, but clinicians and patients both deserve to know that screening can detect lung cancer early, when treatment has the best chance of being beneficial.”
He added that the hope is that these recommendations will encourage clinicians to examine the barriers to effective lung cancer screening in their communities and to do what they can to improve implementation. “We also hope to encourage patients to have conversations with their clinicians about whether they are eligible for screening and to discuss smoking cessation treatments if they are still smoking,” Dr. Wong added.
In an accompanying editorial, Louise M. Henderson, PhD, M. Patricia Rivera, MD, FCCP, and Ethan Basch, MD, all from the University of North Carolina at Chapel Hill, address some of the current challenges in implementation.
They note that reimbursement for lung cancer screening by Medicare requires submission of data to a Centers for Medicare & Medicaid Services–approved registry, and this can present problems for facilities serving less affluent communities or that have limited resources.
Medicaid coverage is also uneven. As of September 2020, lung cancer screening was covered by 38 Medicaid programs, but not by 9. For three programs, data on coverage were not available.
“With the new recommendations lowering the screening-eligible age to 50 years, many eligible individuals who are uninsured or who are receiving Medicaid and living in states that do not cover screening will have financial barriers to undergo screening,” they write.
In addition, many individuals in at-risk populations lack adequate geographic access to comprehensive lung cancer screening programs.
Expanding eligibility criteria is important, the editorialists point out, but barriers to screening, which include lack of insurance coverage and limited physical access to high-quality screening programs, highlight the complex problems with implementation that need to be addressed.
“A concerted effort to increase the reach of lung cancer screening is needed,” they write. “The 2021 USPSTF recommendation statement represents a leap forward in evidence and offers promise to prevent more cancer deaths and address screening disparities. But the greatest work lies ahead to ensure this promise is actualized.”
Advocacy needed
When approached for comment, Jianjun Zhang, MD, PhD, from the department of thoracic/head and neck medical oncology, University of Texas MD Anderson Cancer Center, Houston, said he supports the new guidelines, and they will lower mortality. “The data are pretty strong overall,” he said in an interview.
Although the uptake of screening is currently very low, he pointed out that, even if uptake remains the same, more lives will be saved because eligibility has been expanded. “More people will be getting screened, so it’s a start,” he said.
Aside from factors such as insurance and access, another problem involves primary care. “Time is very limited in primary care,” he said. “You have about 15 minutes, and it can be really hard to fit everything into a visit. Screening may get left out or may only get a brief mention.”
Advocacy is needed, Dr. Zhang pointed out. “Breast cancer has strong voices and advocacy, and people are more aware of mammography,” he said. “The information is disseminated out into the community. We need the same for lung cancer.”
Dr. Zhang emphasized that, even with the expanded criteria, many individuals will still be missed. “There are other risk factors besides smoking,” he said. “About 10% of lung cancers occur in never-smokers.”
Other risk factors include a family history of lung cancer, exposure to certain materials and chemicals, working in the mining industry, and genetics.
“We will move on to more personalized screening at some point,” he said. “But right now, we can’t make it too complicated for patients and doctors. We need to concentrate on increasing screening rates within these current criteria.”
The updated guidelines have been given a B recommendation, meaning the USPSTF recommends that clinicians provide the service to eligible patients, there is at least fair evidence that this service improves important health outcomes, and benefits outweigh harms.
The USPSTF is an independent, voluntary body. The U.S. Congress mandates that the Agency for Healthcare Research and Quality support the operations of the USPSTF. All members of the USPSTF receive travel reimbursement and an honorarium for participating in USPSTF meetings. The original article lists relevant financial relationships of task force members. Dr. Zhang has received grants from Johnson & Johnson and Merck, and adversary/consulting/honoraria fees from AstraZeneca, Bristol-Myers Squibb, GenePlus, Innovent, OrigMed, and Roche.
A version of this article first appeared on Medscape.com.
“This is great news because it means that nearly twice as many people are eligible to be screened, which we hope will allow clinicians to save more lives and help people remain healthy longer,” commented John Wong, MD, chief science officer, vice chair for clinical affairs, and chief of the Division of Clinical Decision Making at USPSTF.
The updated final recommendations were published online on March 9 in JAMA.
The USPSTF recommends annual screening with low-dose CT for adults aged 50-80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.
This updates guidance issued in 2013, which recommended annual screening for lung cancer for adults aged 55-80 years who had a 30 pack-year smoking history and who were either current smokers or had quit within the past 15 years.
The move will nearly double the number of people are now eligible for screening, up to 14.5 million individuals – an increase of 81% (6.4 million adults) from the 2013 recommendations.
The expanded criteria may help increase screening among Black individuals and women. Data show that both groups tend to smoke fewer cigarettes than White men and that Black persons are at higher risk for lung cancer than White persons. In addition, research has shown that about one-third of Black patients with lung cancer were diagnosed before the age of 55 years, which means they would not have been recommended for screening under the previous guidelines.
Uptake has been limited
To date, uptake of lung cancer screening has been very limited, from 6% to 18% of individuals who meet the eligibility criteria.
The new recommendations will open up screening to many more people, but challenges to implementation remain.
“The science is clear that lung cancer screening has the potential to save lives,” Dr. Wong told this news organization. “We recognize that there are existing barriers to screening everyone who is eligible, but clinicians and patients both deserve to know that screening can detect lung cancer early, when treatment has the best chance of being beneficial.”
He added that the hope is that these recommendations will encourage clinicians to examine the barriers to effective lung cancer screening in their communities and to do what they can to improve implementation. “We also hope to encourage patients to have conversations with their clinicians about whether they are eligible for screening and to discuss smoking cessation treatments if they are still smoking,” Dr. Wong added.
In an accompanying editorial, Louise M. Henderson, PhD, M. Patricia Rivera, MD, FCCP, and Ethan Basch, MD, all from the University of North Carolina at Chapel Hill, address some of the current challenges in implementation.
They note that reimbursement for lung cancer screening by Medicare requires submission of data to a Centers for Medicare & Medicaid Services–approved registry, and this can present problems for facilities serving less affluent communities or that have limited resources.
Medicaid coverage is also uneven. As of September 2020, lung cancer screening was covered by 38 Medicaid programs, but not by 9. For three programs, data on coverage were not available.
“With the new recommendations lowering the screening-eligible age to 50 years, many eligible individuals who are uninsured or who are receiving Medicaid and living in states that do not cover screening will have financial barriers to undergo screening,” they write.
In addition, many individuals in at-risk populations lack adequate geographic access to comprehensive lung cancer screening programs.
Expanding eligibility criteria is important, the editorialists point out, but barriers to screening, which include lack of insurance coverage and limited physical access to high-quality screening programs, highlight the complex problems with implementation that need to be addressed.
“A concerted effort to increase the reach of lung cancer screening is needed,” they write. “The 2021 USPSTF recommendation statement represents a leap forward in evidence and offers promise to prevent more cancer deaths and address screening disparities. But the greatest work lies ahead to ensure this promise is actualized.”
Advocacy needed
When approached for comment, Jianjun Zhang, MD, PhD, from the department of thoracic/head and neck medical oncology, University of Texas MD Anderson Cancer Center, Houston, said he supports the new guidelines, and they will lower mortality. “The data are pretty strong overall,” he said in an interview.
Although the uptake of screening is currently very low, he pointed out that, even if uptake remains the same, more lives will be saved because eligibility has been expanded. “More people will be getting screened, so it’s a start,” he said.
Aside from factors such as insurance and access, another problem involves primary care. “Time is very limited in primary care,” he said. “You have about 15 minutes, and it can be really hard to fit everything into a visit. Screening may get left out or may only get a brief mention.”
Advocacy is needed, Dr. Zhang pointed out. “Breast cancer has strong voices and advocacy, and people are more aware of mammography,” he said. “The information is disseminated out into the community. We need the same for lung cancer.”
Dr. Zhang emphasized that, even with the expanded criteria, many individuals will still be missed. “There are other risk factors besides smoking,” he said. “About 10% of lung cancers occur in never-smokers.”
Other risk factors include a family history of lung cancer, exposure to certain materials and chemicals, working in the mining industry, and genetics.
“We will move on to more personalized screening at some point,” he said. “But right now, we can’t make it too complicated for patients and doctors. We need to concentrate on increasing screening rates within these current criteria.”
The updated guidelines have been given a B recommendation, meaning the USPSTF recommends that clinicians provide the service to eligible patients, there is at least fair evidence that this service improves important health outcomes, and benefits outweigh harms.
The USPSTF is an independent, voluntary body. The U.S. Congress mandates that the Agency for Healthcare Research and Quality support the operations of the USPSTF. All members of the USPSTF receive travel reimbursement and an honorarium for participating in USPSTF meetings. The original article lists relevant financial relationships of task force members. Dr. Zhang has received grants from Johnson & Johnson and Merck, and adversary/consulting/honoraria fees from AstraZeneca, Bristol-Myers Squibb, GenePlus, Innovent, OrigMed, and Roche.
A version of this article first appeared on Medscape.com.
Pembrolizumab SCLC indication withdrawn in U.S.
The move does not affect any of the drug’s other indications. The immunotherapy is used in the treatment of many different types of cancer.
The SCLC indication had been granted an accelerated approval by the Food and Drug Administration in 2019 based on tumor response rate and durability of response data from patient cohorts in two trials. However, the anti-PD-1 therapy failed to demonstrate statistically significant improved overall survival in a confirmatory trial, which is mandated after an accelerated approval.
The FDA is conducting “an industry-wide evaluation of indications based on accelerated approvals that have not yet met their postmarketing requirements,” said Merck.
In February of 2021, an indication for durvalumab (Imfinzi) was withdrawn by AstraZeneca in concert with the FDA after the drug failed to improve overall survival in unresectable metastatic bladder cancer in a confirmatory trial, as reported by Medscape Medical News.
“We will continue to rigorously evaluate the benefits of [pembrolizumab] in small cell lung cancer and other types of cancer, in pursuit of Merck’s mission to save and improve lives,” Roy Baynes, MD, chief medical officer, Merck Research Laboratories, said in the company statement
Dr. Baynes also championed the value of accelerated approvals.
“The accelerated pathways created by the FDA have been integral to the remarkable progress in oncology care over the past 5 years and have helped many cancer patients with advanced disease, including small cell lung cancer, access new treatments,” he said.
However, in the past, the FDA has been criticized for approving new cancer drugs based on surrogate markers such as response rates because, in many cases, subsequent studies often show that the drug fails to improve overall survival.
For example, a 2015 study found that 36 (67%) of 54 cancer drug approvals from 2008 to 2012 were made on the basis of surrogate markers – either tumor response rate or progression-free survival. Over a median follow-up period of 4.4 years, only 5 of those 36 drugs were shown in randomized studies to improve overall survival, as reported by Medscape Medical News.
The FDA says that it instituted the accelerated approval program to “allow for earlier approval of drugs that treat serious conditions, and that fill an unmet medical need based on a surrogate endpoint.” The program was started in 1992, in the midst of the HIV/AIDS epidemic.
In 2020, the nonprofit Friends of Cancer Research issued a white paper calling for reform in the accelerated approval process, which included a proposal to add risk assessment to surrogate endpoints that would factor in variables such as toxicity.
A version of this article first appeared on Medscape.com.
The move does not affect any of the drug’s other indications. The immunotherapy is used in the treatment of many different types of cancer.
The SCLC indication had been granted an accelerated approval by the Food and Drug Administration in 2019 based on tumor response rate and durability of response data from patient cohorts in two trials. However, the anti-PD-1 therapy failed to demonstrate statistically significant improved overall survival in a confirmatory trial, which is mandated after an accelerated approval.
The FDA is conducting “an industry-wide evaluation of indications based on accelerated approvals that have not yet met their postmarketing requirements,” said Merck.
In February of 2021, an indication for durvalumab (Imfinzi) was withdrawn by AstraZeneca in concert with the FDA after the drug failed to improve overall survival in unresectable metastatic bladder cancer in a confirmatory trial, as reported by Medscape Medical News.
“We will continue to rigorously evaluate the benefits of [pembrolizumab] in small cell lung cancer and other types of cancer, in pursuit of Merck’s mission to save and improve lives,” Roy Baynes, MD, chief medical officer, Merck Research Laboratories, said in the company statement
Dr. Baynes also championed the value of accelerated approvals.
“The accelerated pathways created by the FDA have been integral to the remarkable progress in oncology care over the past 5 years and have helped many cancer patients with advanced disease, including small cell lung cancer, access new treatments,” he said.
However, in the past, the FDA has been criticized for approving new cancer drugs based on surrogate markers such as response rates because, in many cases, subsequent studies often show that the drug fails to improve overall survival.
For example, a 2015 study found that 36 (67%) of 54 cancer drug approvals from 2008 to 2012 were made on the basis of surrogate markers – either tumor response rate or progression-free survival. Over a median follow-up period of 4.4 years, only 5 of those 36 drugs were shown in randomized studies to improve overall survival, as reported by Medscape Medical News.
The FDA says that it instituted the accelerated approval program to “allow for earlier approval of drugs that treat serious conditions, and that fill an unmet medical need based on a surrogate endpoint.” The program was started in 1992, in the midst of the HIV/AIDS epidemic.
In 2020, the nonprofit Friends of Cancer Research issued a white paper calling for reform in the accelerated approval process, which included a proposal to add risk assessment to surrogate endpoints that would factor in variables such as toxicity.
A version of this article first appeared on Medscape.com.
The move does not affect any of the drug’s other indications. The immunotherapy is used in the treatment of many different types of cancer.
The SCLC indication had been granted an accelerated approval by the Food and Drug Administration in 2019 based on tumor response rate and durability of response data from patient cohorts in two trials. However, the anti-PD-1 therapy failed to demonstrate statistically significant improved overall survival in a confirmatory trial, which is mandated after an accelerated approval.
The FDA is conducting “an industry-wide evaluation of indications based on accelerated approvals that have not yet met their postmarketing requirements,” said Merck.
In February of 2021, an indication for durvalumab (Imfinzi) was withdrawn by AstraZeneca in concert with the FDA after the drug failed to improve overall survival in unresectable metastatic bladder cancer in a confirmatory trial, as reported by Medscape Medical News.
“We will continue to rigorously evaluate the benefits of [pembrolizumab] in small cell lung cancer and other types of cancer, in pursuit of Merck’s mission to save and improve lives,” Roy Baynes, MD, chief medical officer, Merck Research Laboratories, said in the company statement
Dr. Baynes also championed the value of accelerated approvals.
“The accelerated pathways created by the FDA have been integral to the remarkable progress in oncology care over the past 5 years and have helped many cancer patients with advanced disease, including small cell lung cancer, access new treatments,” he said.
However, in the past, the FDA has been criticized for approving new cancer drugs based on surrogate markers such as response rates because, in many cases, subsequent studies often show that the drug fails to improve overall survival.
For example, a 2015 study found that 36 (67%) of 54 cancer drug approvals from 2008 to 2012 were made on the basis of surrogate markers – either tumor response rate or progression-free survival. Over a median follow-up period of 4.4 years, only 5 of those 36 drugs were shown in randomized studies to improve overall survival, as reported by Medscape Medical News.
The FDA says that it instituted the accelerated approval program to “allow for earlier approval of drugs that treat serious conditions, and that fill an unmet medical need based on a surrogate endpoint.” The program was started in 1992, in the midst of the HIV/AIDS epidemic.
In 2020, the nonprofit Friends of Cancer Research issued a white paper calling for reform in the accelerated approval process, which included a proposal to add risk assessment to surrogate endpoints that would factor in variables such as toxicity.
A version of this article first appeared on Medscape.com.
Study: Shared decision-making in lung cancer screening needs work
Shared decision-making is an integral step in lung cancer screening with low-dose CT (LDCT) in high-risk patients, but a cross-sectional study at two academic medical centers in Texas has found wide variability in the quality of shared decision-making encounters and that nearly a third of patients reported being conflicted about their decisions to pursue screening.
Lead author Shawn P.E. Nishi, MD, associate professor in the division of pulmonary critical care and sleep medicine, department of internal medicine, of the University of Texas Medical Branch, Galveston, noted two striking findings of the study, published in Chest: that physicians rarely used decision aids according to Centers for Medicare & Medicaid Services direction, and that a “considerable imbalance” exists in the way physicians present management choices to patients. “As physicians, we want to focus on the positive,” she said, “but in shared decision-making (SDM) there needs to be a better balance between presentation and understanding of the risks and the benefits of lung cancer screening (LCS).”
Since 2015, CMS has reimbursed for LCS counseling and an shared decision-making visit before a patient has the screening.
The study analyzed self-reported survey results of 266 patients who had been through SDM at UTMB Galveston and MD Anderson Cancer Center in Houston in 2017. They completed patient surveys the following year. The study population was 87% White, 38% had a family history of lung cancer, and 39% were current smokers. The mean pack-year history was 40.4 years.
A high percentage – 86.6% – said they were satisfied with the level in which they were involved in their screening decision. Patients reported that their doctors talked to them about the benefits of LCS far more frequently than the potential harms, 68.3% to 20.8%. And 12.5% said they understood that an abnormal scan was likely to result in a negative finding. Only 30.7% said they’d received educational materials about LCS during the screening process.
A year after completing the SDM process, their knowledge of LCS was variable at best; on average, they answered 41.4% of the questions correctly, and almost one-third (31%) indicated that screening, rather than quitting smoking, was the best way reduce their lung cancer risk.
The study noted that, for patients who derive a small benefit from LCS, the absolute risk reduction is only 0.3%, which may not be enough to offset the potential harms of LDCT.
“The LCS exam itself is a simple noninvasive procedure; you get a scan and go about your day once it’s read,” Dr. Nishi said. “However there is a high false-positive rate, and the question really becomes that, as you start to work up those false positives and even true positives, however small, there is a risk associated with every procedure or evaluation thereafter. So the shared decision-making process is really there to ensure that patients value finding their lung cancer early if they do have it versus the potential harms down the line.”
However, as this study points out, there aren’t many parameters for what SDM entails. “It’s more than just an information exchange back and forth,” Dr. Nishi said. “It’s about having good-quality communication between the provider and patients so that the right decision can ultimately be made for each patient. It takes a very dedicated person that can commit the time and expertise to it. I don’t think that it should be taken lightly.”
As Dr. Nishi and colleagues pointed out in their study, SDM incorporates three essential elements: recognizing and acknowledging that a decision has to be made, knowing and understanding the best available evidence, and incorporating the patient’s own values and preferences in the decision.
CMS outlines specific components of SDM. It includes, beyond a discussion of the potential benefits and harms and use of a decision aid, education on the need for adherence to annual screening, and counseling on either stopping smoking or continued abstinence.
For physicians, dedicating the time and energy SDM needs can be a challenge, Dr. Nishi noted, “Health care doesn’t have a lot of support to perform shared decision-making,” she said. “In a very busy practice it’s very hard to make sure you have a good process where you can sit down and take all the time you need with a patient to open up a dialog about the risks and benefits.”
After they completed the screening process, 33.6% of patients said they had some conflicting feelings about their decision. Non-White patients were about four times more likely than White patients to feel conflicted about their choices (odds ratio, 4.31; 95% confidence interval, 1.36-13.70), as were former smokers, compared with current smokers (OR, 1.93; 95% CI, 1.04-3.55).
Future studies of SDM in LCS should focus on outcomes, said Dr. Nishi. “Hopefully then we can focus on those things that benefit patients the most.”
Abbie Begnaud, MD, FCCP, a pulmonologist at the University of Minnesota, Minneapolis, said this study confirmed what other studies found about shortcomings of SDM, with one difference. “We already knew we were not doing a great job at shared decision-making,” she said. “To me, the difference in this study is that most of the patients were pretty satisfied with their degree of involvement.”
She noted the low percentage of patients who understood that abnormal scans may be noncancerous. “This is one area that I think is an important place for us to improve,” Dr. Begnaud said.
The findings about non-White patients and former smokers are also telling, Dr. Begnaud said. “This highlights that we need to pay close attention to these two groups – people who have traditionally, historically been marginalized in medical care – and provide them the support they need to make a decision.”
Dr. Nishi and colleagues have no relevant disclosures. The study was supported by the Cancer Prevention and Research Institute of Texas and received grants from the National Cancer Institute and the University of Texas MD Anderson Cancer Center Duncan Family Institute for Cancer Prevention and Risk Assessment. Dr. Begnaud has no relevant relationships to disclose.
Shared decision-making is an integral step in lung cancer screening with low-dose CT (LDCT) in high-risk patients, but a cross-sectional study at two academic medical centers in Texas has found wide variability in the quality of shared decision-making encounters and that nearly a third of patients reported being conflicted about their decisions to pursue screening.
Lead author Shawn P.E. Nishi, MD, associate professor in the division of pulmonary critical care and sleep medicine, department of internal medicine, of the University of Texas Medical Branch, Galveston, noted two striking findings of the study, published in Chest: that physicians rarely used decision aids according to Centers for Medicare & Medicaid Services direction, and that a “considerable imbalance” exists in the way physicians present management choices to patients. “As physicians, we want to focus on the positive,” she said, “but in shared decision-making (SDM) there needs to be a better balance between presentation and understanding of the risks and the benefits of lung cancer screening (LCS).”
Since 2015, CMS has reimbursed for LCS counseling and an shared decision-making visit before a patient has the screening.
The study analyzed self-reported survey results of 266 patients who had been through SDM at UTMB Galveston and MD Anderson Cancer Center in Houston in 2017. They completed patient surveys the following year. The study population was 87% White, 38% had a family history of lung cancer, and 39% were current smokers. The mean pack-year history was 40.4 years.
A high percentage – 86.6% – said they were satisfied with the level in which they were involved in their screening decision. Patients reported that their doctors talked to them about the benefits of LCS far more frequently than the potential harms, 68.3% to 20.8%. And 12.5% said they understood that an abnormal scan was likely to result in a negative finding. Only 30.7% said they’d received educational materials about LCS during the screening process.
A year after completing the SDM process, their knowledge of LCS was variable at best; on average, they answered 41.4% of the questions correctly, and almost one-third (31%) indicated that screening, rather than quitting smoking, was the best way reduce their lung cancer risk.
The study noted that, for patients who derive a small benefit from LCS, the absolute risk reduction is only 0.3%, which may not be enough to offset the potential harms of LDCT.
“The LCS exam itself is a simple noninvasive procedure; you get a scan and go about your day once it’s read,” Dr. Nishi said. “However there is a high false-positive rate, and the question really becomes that, as you start to work up those false positives and even true positives, however small, there is a risk associated with every procedure or evaluation thereafter. So the shared decision-making process is really there to ensure that patients value finding their lung cancer early if they do have it versus the potential harms down the line.”
However, as this study points out, there aren’t many parameters for what SDM entails. “It’s more than just an information exchange back and forth,” Dr. Nishi said. “It’s about having good-quality communication between the provider and patients so that the right decision can ultimately be made for each patient. It takes a very dedicated person that can commit the time and expertise to it. I don’t think that it should be taken lightly.”
As Dr. Nishi and colleagues pointed out in their study, SDM incorporates three essential elements: recognizing and acknowledging that a decision has to be made, knowing and understanding the best available evidence, and incorporating the patient’s own values and preferences in the decision.
CMS outlines specific components of SDM. It includes, beyond a discussion of the potential benefits and harms and use of a decision aid, education on the need for adherence to annual screening, and counseling on either stopping smoking or continued abstinence.
For physicians, dedicating the time and energy SDM needs can be a challenge, Dr. Nishi noted, “Health care doesn’t have a lot of support to perform shared decision-making,” she said. “In a very busy practice it’s very hard to make sure you have a good process where you can sit down and take all the time you need with a patient to open up a dialog about the risks and benefits.”
After they completed the screening process, 33.6% of patients said they had some conflicting feelings about their decision. Non-White patients were about four times more likely than White patients to feel conflicted about their choices (odds ratio, 4.31; 95% confidence interval, 1.36-13.70), as were former smokers, compared with current smokers (OR, 1.93; 95% CI, 1.04-3.55).
Future studies of SDM in LCS should focus on outcomes, said Dr. Nishi. “Hopefully then we can focus on those things that benefit patients the most.”
Abbie Begnaud, MD, FCCP, a pulmonologist at the University of Minnesota, Minneapolis, said this study confirmed what other studies found about shortcomings of SDM, with one difference. “We already knew we were not doing a great job at shared decision-making,” she said. “To me, the difference in this study is that most of the patients were pretty satisfied with their degree of involvement.”
She noted the low percentage of patients who understood that abnormal scans may be noncancerous. “This is one area that I think is an important place for us to improve,” Dr. Begnaud said.
The findings about non-White patients and former smokers are also telling, Dr. Begnaud said. “This highlights that we need to pay close attention to these two groups – people who have traditionally, historically been marginalized in medical care – and provide them the support they need to make a decision.”
Dr. Nishi and colleagues have no relevant disclosures. The study was supported by the Cancer Prevention and Research Institute of Texas and received grants from the National Cancer Institute and the University of Texas MD Anderson Cancer Center Duncan Family Institute for Cancer Prevention and Risk Assessment. Dr. Begnaud has no relevant relationships to disclose.
Shared decision-making is an integral step in lung cancer screening with low-dose CT (LDCT) in high-risk patients, but a cross-sectional study at two academic medical centers in Texas has found wide variability in the quality of shared decision-making encounters and that nearly a third of patients reported being conflicted about their decisions to pursue screening.
Lead author Shawn P.E. Nishi, MD, associate professor in the division of pulmonary critical care and sleep medicine, department of internal medicine, of the University of Texas Medical Branch, Galveston, noted two striking findings of the study, published in Chest: that physicians rarely used decision aids according to Centers for Medicare & Medicaid Services direction, and that a “considerable imbalance” exists in the way physicians present management choices to patients. “As physicians, we want to focus on the positive,” she said, “but in shared decision-making (SDM) there needs to be a better balance between presentation and understanding of the risks and the benefits of lung cancer screening (LCS).”
Since 2015, CMS has reimbursed for LCS counseling and an shared decision-making visit before a patient has the screening.
The study analyzed self-reported survey results of 266 patients who had been through SDM at UTMB Galveston and MD Anderson Cancer Center in Houston in 2017. They completed patient surveys the following year. The study population was 87% White, 38% had a family history of lung cancer, and 39% were current smokers. The mean pack-year history was 40.4 years.
A high percentage – 86.6% – said they were satisfied with the level in which they were involved in their screening decision. Patients reported that their doctors talked to them about the benefits of LCS far more frequently than the potential harms, 68.3% to 20.8%. And 12.5% said they understood that an abnormal scan was likely to result in a negative finding. Only 30.7% said they’d received educational materials about LCS during the screening process.
A year after completing the SDM process, their knowledge of LCS was variable at best; on average, they answered 41.4% of the questions correctly, and almost one-third (31%) indicated that screening, rather than quitting smoking, was the best way reduce their lung cancer risk.
The study noted that, for patients who derive a small benefit from LCS, the absolute risk reduction is only 0.3%, which may not be enough to offset the potential harms of LDCT.
“The LCS exam itself is a simple noninvasive procedure; you get a scan and go about your day once it’s read,” Dr. Nishi said. “However there is a high false-positive rate, and the question really becomes that, as you start to work up those false positives and even true positives, however small, there is a risk associated with every procedure or evaluation thereafter. So the shared decision-making process is really there to ensure that patients value finding their lung cancer early if they do have it versus the potential harms down the line.”
However, as this study points out, there aren’t many parameters for what SDM entails. “It’s more than just an information exchange back and forth,” Dr. Nishi said. “It’s about having good-quality communication between the provider and patients so that the right decision can ultimately be made for each patient. It takes a very dedicated person that can commit the time and expertise to it. I don’t think that it should be taken lightly.”
As Dr. Nishi and colleagues pointed out in their study, SDM incorporates three essential elements: recognizing and acknowledging that a decision has to be made, knowing and understanding the best available evidence, and incorporating the patient’s own values and preferences in the decision.
CMS outlines specific components of SDM. It includes, beyond a discussion of the potential benefits and harms and use of a decision aid, education on the need for adherence to annual screening, and counseling on either stopping smoking or continued abstinence.
For physicians, dedicating the time and energy SDM needs can be a challenge, Dr. Nishi noted, “Health care doesn’t have a lot of support to perform shared decision-making,” she said. “In a very busy practice it’s very hard to make sure you have a good process where you can sit down and take all the time you need with a patient to open up a dialog about the risks and benefits.”
After they completed the screening process, 33.6% of patients said they had some conflicting feelings about their decision. Non-White patients were about four times more likely than White patients to feel conflicted about their choices (odds ratio, 4.31; 95% confidence interval, 1.36-13.70), as were former smokers, compared with current smokers (OR, 1.93; 95% CI, 1.04-3.55).
Future studies of SDM in LCS should focus on outcomes, said Dr. Nishi. “Hopefully then we can focus on those things that benefit patients the most.”
Abbie Begnaud, MD, FCCP, a pulmonologist at the University of Minnesota, Minneapolis, said this study confirmed what other studies found about shortcomings of SDM, with one difference. “We already knew we were not doing a great job at shared decision-making,” she said. “To me, the difference in this study is that most of the patients were pretty satisfied with their degree of involvement.”
She noted the low percentage of patients who understood that abnormal scans may be noncancerous. “This is one area that I think is an important place for us to improve,” Dr. Begnaud said.
The findings about non-White patients and former smokers are also telling, Dr. Begnaud said. “This highlights that we need to pay close attention to these two groups – people who have traditionally, historically been marginalized in medical care – and provide them the support they need to make a decision.”
Dr. Nishi and colleagues have no relevant disclosures. The study was supported by the Cancer Prevention and Research Institute of Texas and received grants from the National Cancer Institute and the University of Texas MD Anderson Cancer Center Duncan Family Institute for Cancer Prevention and Risk Assessment. Dr. Begnaud has no relevant relationships to disclose.
FROM CHEST
Researchers identify four small cell lung cancer subtypes and their best therapies
Researchers studying a large set of small cell lung cancer (SCLC) tumor samples have identified four SCLC subtypes, and they propose that matching baseline tumor subtypes to SCLC therapy may enhance the depth and duration of response.
Carl M. Gay, MD, PhD, of University of Texas MD Anderson Cancer Center in Houston, and colleagues conducted this research and described their findings in Cancer Cell.
The authors noted that survival rates in SCLC remain dismal despite recent modest gains in progression-free survival and overall survival achieved through adding immunotherapy to platinum-based frontline chemotherapy.
Based on transcription factors indicating which genes are activated, prior research had already identified three possible SCLC subtypes. Many SCLC tumors, however, do not fit into one of these three groups, the authors said.
Inflamed gene signature
The four groups were identified using tumor expression data and nonnegative matrix factorization from published sources on 81 SCLC patients, and then validated via the largest SCLC data set available (276 SCLC patients enrolled in the phase 3 IMpower133 trial).
The SCLC subtypes were defined largely by differential expression of transcription factors – subtype SCLC-A by ASCL1, subtype SCLC-N by NEUROD1, and subtype SCLC-P by POU2F3. The fourth subtype, SCLC-I, is characterized by low expression of all three transcription factor signatures and an inflamed gene signature with a high expression of multiple immune genes, including significantly greater levels of genes indicating the presence of CD8-positive cytotoxic T cells.
Because each subtype demonstrates unique vulnerability to investigational therapies, this subtype classification has significant clinical implications.
“We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients,” the authors stated.
“Our paper shows that the inflamed group has a distinct biology and environment and tends to be more responsive to immunotherapy,” study author Lauren Averett Byers, MD, also of the University of Texas MD Anderson Cancer Center, stated in a press release. “Identifying the inflamed group is very important because, so far, there have not been any validated biomarkers for small cell lung cancer that predict which patients get the most benefit from immunotherapy.”
In samples from the other three subtypes, SCLC-A was most responsive to BCL2 inhibitors, SCLC-N to Aurora kinase inhibitors, and SCLC-P to PARP inhibitors.
Treatment resistance
The tendency of SCLC to develop treatment resistance, even after an initial response, is a known challenge. Using single-cell RNA sequencing to evaluate tumor evolution, the authors observed a tendency of SCLC-A to switch to SCLC-I after chemotherapy treatment, a possible contributor to treatment resistance.
It will be necessary to verify the study findings through further investigations, particularly regarding the therapeutic vulnerabilities for each group.
“Now we can develop more effective strategies for each group in clinical trials, taking into account that they each have different biology and optimal drug targets,” Dr. Byers said. “As a field, small cell lung cancer is about 15 years behind non–small cell lung cancer’s renaissance of biomarkers and personalized therapies. This represents a huge step in understanding which drugs work best for which patients and gives us a path forward for personalized approaches for small cell lung cancer.”
“Dr. Gay’s work is the latest in a growing series of exciting studies demonstrating the utility of defining subtypes of small cell lung cancer based on expression of master transcriptional regulators,” commented Charles Rudin, MD, PhD, of Memorial Sloan Kettering Cancer Center in New York, in an interview.
He added, “While tumors can evolve between some of these categories, the dominant subtype assignment influences therapeutic vulnerabilities. It is an exciting time for those of us engaged in small cell research. Subtyping should help guide more focused and successful clinical trials for patients with small cell lung cancer.”
The authors disclosed multiple relationships with companies. The study was supported by the National Institutes of Health/National Cancer Institute, the University of Texas Southwestern and MD Anderson Cancer Center, and a variety of other governmental and nonprofit groups. Dr. Rudin is principal investigator of the NCI small cell lung cancer research consortium.
Researchers studying a large set of small cell lung cancer (SCLC) tumor samples have identified four SCLC subtypes, and they propose that matching baseline tumor subtypes to SCLC therapy may enhance the depth and duration of response.
Carl M. Gay, MD, PhD, of University of Texas MD Anderson Cancer Center in Houston, and colleagues conducted this research and described their findings in Cancer Cell.
The authors noted that survival rates in SCLC remain dismal despite recent modest gains in progression-free survival and overall survival achieved through adding immunotherapy to platinum-based frontline chemotherapy.
Based on transcription factors indicating which genes are activated, prior research had already identified three possible SCLC subtypes. Many SCLC tumors, however, do not fit into one of these three groups, the authors said.
Inflamed gene signature
The four groups were identified using tumor expression data and nonnegative matrix factorization from published sources on 81 SCLC patients, and then validated via the largest SCLC data set available (276 SCLC patients enrolled in the phase 3 IMpower133 trial).
The SCLC subtypes were defined largely by differential expression of transcription factors – subtype SCLC-A by ASCL1, subtype SCLC-N by NEUROD1, and subtype SCLC-P by POU2F3. The fourth subtype, SCLC-I, is characterized by low expression of all three transcription factor signatures and an inflamed gene signature with a high expression of multiple immune genes, including significantly greater levels of genes indicating the presence of CD8-positive cytotoxic T cells.
Because each subtype demonstrates unique vulnerability to investigational therapies, this subtype classification has significant clinical implications.
“We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients,” the authors stated.
“Our paper shows that the inflamed group has a distinct biology and environment and tends to be more responsive to immunotherapy,” study author Lauren Averett Byers, MD, also of the University of Texas MD Anderson Cancer Center, stated in a press release. “Identifying the inflamed group is very important because, so far, there have not been any validated biomarkers for small cell lung cancer that predict which patients get the most benefit from immunotherapy.”
In samples from the other three subtypes, SCLC-A was most responsive to BCL2 inhibitors, SCLC-N to Aurora kinase inhibitors, and SCLC-P to PARP inhibitors.
Treatment resistance
The tendency of SCLC to develop treatment resistance, even after an initial response, is a known challenge. Using single-cell RNA sequencing to evaluate tumor evolution, the authors observed a tendency of SCLC-A to switch to SCLC-I after chemotherapy treatment, a possible contributor to treatment resistance.
It will be necessary to verify the study findings through further investigations, particularly regarding the therapeutic vulnerabilities for each group.
“Now we can develop more effective strategies for each group in clinical trials, taking into account that they each have different biology and optimal drug targets,” Dr. Byers said. “As a field, small cell lung cancer is about 15 years behind non–small cell lung cancer’s renaissance of biomarkers and personalized therapies. This represents a huge step in understanding which drugs work best for which patients and gives us a path forward for personalized approaches for small cell lung cancer.”
“Dr. Gay’s work is the latest in a growing series of exciting studies demonstrating the utility of defining subtypes of small cell lung cancer based on expression of master transcriptional regulators,” commented Charles Rudin, MD, PhD, of Memorial Sloan Kettering Cancer Center in New York, in an interview.
He added, “While tumors can evolve between some of these categories, the dominant subtype assignment influences therapeutic vulnerabilities. It is an exciting time for those of us engaged in small cell research. Subtyping should help guide more focused and successful clinical trials for patients with small cell lung cancer.”
The authors disclosed multiple relationships with companies. The study was supported by the National Institutes of Health/National Cancer Institute, the University of Texas Southwestern and MD Anderson Cancer Center, and a variety of other governmental and nonprofit groups. Dr. Rudin is principal investigator of the NCI small cell lung cancer research consortium.
Researchers studying a large set of small cell lung cancer (SCLC) tumor samples have identified four SCLC subtypes, and they propose that matching baseline tumor subtypes to SCLC therapy may enhance the depth and duration of response.
Carl M. Gay, MD, PhD, of University of Texas MD Anderson Cancer Center in Houston, and colleagues conducted this research and described their findings in Cancer Cell.
The authors noted that survival rates in SCLC remain dismal despite recent modest gains in progression-free survival and overall survival achieved through adding immunotherapy to platinum-based frontline chemotherapy.
Based on transcription factors indicating which genes are activated, prior research had already identified three possible SCLC subtypes. Many SCLC tumors, however, do not fit into one of these three groups, the authors said.
Inflamed gene signature
The four groups were identified using tumor expression data and nonnegative matrix factorization from published sources on 81 SCLC patients, and then validated via the largest SCLC data set available (276 SCLC patients enrolled in the phase 3 IMpower133 trial).
The SCLC subtypes were defined largely by differential expression of transcription factors – subtype SCLC-A by ASCL1, subtype SCLC-N by NEUROD1, and subtype SCLC-P by POU2F3. The fourth subtype, SCLC-I, is characterized by low expression of all three transcription factor signatures and an inflamed gene signature with a high expression of multiple immune genes, including significantly greater levels of genes indicating the presence of CD8-positive cytotoxic T cells.
Because each subtype demonstrates unique vulnerability to investigational therapies, this subtype classification has significant clinical implications.
“We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients,” the authors stated.
“Our paper shows that the inflamed group has a distinct biology and environment and tends to be more responsive to immunotherapy,” study author Lauren Averett Byers, MD, also of the University of Texas MD Anderson Cancer Center, stated in a press release. “Identifying the inflamed group is very important because, so far, there have not been any validated biomarkers for small cell lung cancer that predict which patients get the most benefit from immunotherapy.”
In samples from the other three subtypes, SCLC-A was most responsive to BCL2 inhibitors, SCLC-N to Aurora kinase inhibitors, and SCLC-P to PARP inhibitors.
Treatment resistance
The tendency of SCLC to develop treatment resistance, even after an initial response, is a known challenge. Using single-cell RNA sequencing to evaluate tumor evolution, the authors observed a tendency of SCLC-A to switch to SCLC-I after chemotherapy treatment, a possible contributor to treatment resistance.
It will be necessary to verify the study findings through further investigations, particularly regarding the therapeutic vulnerabilities for each group.
“Now we can develop more effective strategies for each group in clinical trials, taking into account that they each have different biology and optimal drug targets,” Dr. Byers said. “As a field, small cell lung cancer is about 15 years behind non–small cell lung cancer’s renaissance of biomarkers and personalized therapies. This represents a huge step in understanding which drugs work best for which patients and gives us a path forward for personalized approaches for small cell lung cancer.”
“Dr. Gay’s work is the latest in a growing series of exciting studies demonstrating the utility of defining subtypes of small cell lung cancer based on expression of master transcriptional regulators,” commented Charles Rudin, MD, PhD, of Memorial Sloan Kettering Cancer Center in New York, in an interview.
He added, “While tumors can evolve between some of these categories, the dominant subtype assignment influences therapeutic vulnerabilities. It is an exciting time for those of us engaged in small cell research. Subtyping should help guide more focused and successful clinical trials for patients with small cell lung cancer.”
The authors disclosed multiple relationships with companies. The study was supported by the National Institutes of Health/National Cancer Institute, the University of Texas Southwestern and MD Anderson Cancer Center, and a variety of other governmental and nonprofit groups. Dr. Rudin is principal investigator of the NCI small cell lung cancer research consortium.
FROM CANCER CELL
FDA approves cemiplimab-rwlc for NSCLC with PD-L1 expression
Specifically, the indication is for first-line treatment as monotherapy for patients with locally advanced or metastatic disease who are not candidates for surgical resection or definitive chemoradiotherapy and whose tumors have a high expression of programmed death–ligand 1 (PD-L1) (Tumor Proportion Score >50%), as determined by an FDA-approved test, with no EGFR, ALK, or ROS1 aberrations.
This is the third indication for cemiplimab-rlwc, a monoclonal antibody and PD-1 inhibitor.
In February, it was approved as the first immunotherapy to treat patients with locally advanced or metastatic basal cell carcinoma that was previously treated with a hedgehog pathway inhibitor or for whom a hedgehog inhibitor is inappropriate.
Cemiplimab-rlwc previously received FDA approval in 2018 for locally advanced or metastatic cutaneous squamous cell carcinoma for patients who were not eligible for curative surgery or radiotherapy. At the time, Karl Lewis, MD, a professor at the University of Colorado at Denver, Aurora, and a trial investigator, predicted that the drug “will change the treatment paradigm for patients with advanced basal cell carcinoma.”
Outperforms chemotherapy
The approval for use in NSCLC is based on results from the phase 3, open-label EMPOWER-Lung 1 trial, which randomly assigned 710 patients in a 1:1 ratio to receive either cemiplimab-rwlc or platinum-doublet chemotherapy. Patients had either locally advanced NSCLC and were not candidates for surgical resection or definitive chemoradiotherapy, or they had metastatic NSCLC.
Patients in the experimental arm received cemiplimab-rwlc 350 mg intravenously every 3 weeks. The primary efficacy outcome measures were overall survival (OS) and progression-free survival (PFS), determined on the basis of blinded independent central review.
Results showed statistically significant improvements in both outcomes. Median OS was 22.1 months with cemiplimab-rwlc versus 14.3 months with chemotherapy (hazard ratio, 0.68; P = .0022). Median PFS was 6.2 months versus 5.6 months (HR, 0.59; P < .0001).
The confirmed overall response rate was 37% for the cemiplimab arm versus 21% for the chemotherapy arm.
The most common adverse reactions (>10%) with cemiplimab-rlwc were musculoskeletal pain, rash, anemia, fatigue, decreased appetite, pneumonia, and cough.
This approval “means physicians and patients have a potent new treatment option against this deadly disease,” said Naiyer Rizvi, MD, Price Family Professor of Medicine, director of thoracic oncology, and codirector of cancer immunotherapy at Columbia University Irving Medical Center, New York, in a statement. He was a steering committee member on the EMPOWER-Lung-1 Trial.
“Notably, Libtayo was approved based on a pivotal trial where most chemotherapy patients crossed over to Libtayo following disease progression, and that allowed for frequently underrepresented patients who had pretreated and clinically stable brain metastases or who had locally advanced disease and were not candidates for definitive chemoradiation,” said Dr. Rizvi. “This gives doctors important new data when considering Libtayo for the varied patients and situations they treat in daily clinical practice.”
A version of this article first appeared on Medscape.com.
Specifically, the indication is for first-line treatment as monotherapy for patients with locally advanced or metastatic disease who are not candidates for surgical resection or definitive chemoradiotherapy and whose tumors have a high expression of programmed death–ligand 1 (PD-L1) (Tumor Proportion Score >50%), as determined by an FDA-approved test, with no EGFR, ALK, or ROS1 aberrations.
This is the third indication for cemiplimab-rlwc, a monoclonal antibody and PD-1 inhibitor.
In February, it was approved as the first immunotherapy to treat patients with locally advanced or metastatic basal cell carcinoma that was previously treated with a hedgehog pathway inhibitor or for whom a hedgehog inhibitor is inappropriate.
Cemiplimab-rlwc previously received FDA approval in 2018 for locally advanced or metastatic cutaneous squamous cell carcinoma for patients who were not eligible for curative surgery or radiotherapy. At the time, Karl Lewis, MD, a professor at the University of Colorado at Denver, Aurora, and a trial investigator, predicted that the drug “will change the treatment paradigm for patients with advanced basal cell carcinoma.”
Outperforms chemotherapy
The approval for use in NSCLC is based on results from the phase 3, open-label EMPOWER-Lung 1 trial, which randomly assigned 710 patients in a 1:1 ratio to receive either cemiplimab-rwlc or platinum-doublet chemotherapy. Patients had either locally advanced NSCLC and were not candidates for surgical resection or definitive chemoradiotherapy, or they had metastatic NSCLC.
Patients in the experimental arm received cemiplimab-rwlc 350 mg intravenously every 3 weeks. The primary efficacy outcome measures were overall survival (OS) and progression-free survival (PFS), determined on the basis of blinded independent central review.
Results showed statistically significant improvements in both outcomes. Median OS was 22.1 months with cemiplimab-rwlc versus 14.3 months with chemotherapy (hazard ratio, 0.68; P = .0022). Median PFS was 6.2 months versus 5.6 months (HR, 0.59; P < .0001).
The confirmed overall response rate was 37% for the cemiplimab arm versus 21% for the chemotherapy arm.
The most common adverse reactions (>10%) with cemiplimab-rlwc were musculoskeletal pain, rash, anemia, fatigue, decreased appetite, pneumonia, and cough.
This approval “means physicians and patients have a potent new treatment option against this deadly disease,” said Naiyer Rizvi, MD, Price Family Professor of Medicine, director of thoracic oncology, and codirector of cancer immunotherapy at Columbia University Irving Medical Center, New York, in a statement. He was a steering committee member on the EMPOWER-Lung-1 Trial.
“Notably, Libtayo was approved based on a pivotal trial where most chemotherapy patients crossed over to Libtayo following disease progression, and that allowed for frequently underrepresented patients who had pretreated and clinically stable brain metastases or who had locally advanced disease and were not candidates for definitive chemoradiation,” said Dr. Rizvi. “This gives doctors important new data when considering Libtayo for the varied patients and situations they treat in daily clinical practice.”
A version of this article first appeared on Medscape.com.
Specifically, the indication is for first-line treatment as monotherapy for patients with locally advanced or metastatic disease who are not candidates for surgical resection or definitive chemoradiotherapy and whose tumors have a high expression of programmed death–ligand 1 (PD-L1) (Tumor Proportion Score >50%), as determined by an FDA-approved test, with no EGFR, ALK, or ROS1 aberrations.
This is the third indication for cemiplimab-rlwc, a monoclonal antibody and PD-1 inhibitor.
In February, it was approved as the first immunotherapy to treat patients with locally advanced or metastatic basal cell carcinoma that was previously treated with a hedgehog pathway inhibitor or for whom a hedgehog inhibitor is inappropriate.
Cemiplimab-rlwc previously received FDA approval in 2018 for locally advanced or metastatic cutaneous squamous cell carcinoma for patients who were not eligible for curative surgery or radiotherapy. At the time, Karl Lewis, MD, a professor at the University of Colorado at Denver, Aurora, and a trial investigator, predicted that the drug “will change the treatment paradigm for patients with advanced basal cell carcinoma.”
Outperforms chemotherapy
The approval for use in NSCLC is based on results from the phase 3, open-label EMPOWER-Lung 1 trial, which randomly assigned 710 patients in a 1:1 ratio to receive either cemiplimab-rwlc or platinum-doublet chemotherapy. Patients had either locally advanced NSCLC and were not candidates for surgical resection or definitive chemoradiotherapy, or they had metastatic NSCLC.
Patients in the experimental arm received cemiplimab-rwlc 350 mg intravenously every 3 weeks. The primary efficacy outcome measures were overall survival (OS) and progression-free survival (PFS), determined on the basis of blinded independent central review.
Results showed statistically significant improvements in both outcomes. Median OS was 22.1 months with cemiplimab-rwlc versus 14.3 months with chemotherapy (hazard ratio, 0.68; P = .0022). Median PFS was 6.2 months versus 5.6 months (HR, 0.59; P < .0001).
The confirmed overall response rate was 37% for the cemiplimab arm versus 21% for the chemotherapy arm.
The most common adverse reactions (>10%) with cemiplimab-rlwc were musculoskeletal pain, rash, anemia, fatigue, decreased appetite, pneumonia, and cough.
This approval “means physicians and patients have a potent new treatment option against this deadly disease,” said Naiyer Rizvi, MD, Price Family Professor of Medicine, director of thoracic oncology, and codirector of cancer immunotherapy at Columbia University Irving Medical Center, New York, in a statement. He was a steering committee member on the EMPOWER-Lung-1 Trial.
“Notably, Libtayo was approved based on a pivotal trial where most chemotherapy patients crossed over to Libtayo following disease progression, and that allowed for frequently underrepresented patients who had pretreated and clinically stable brain metastases or who had locally advanced disease and were not candidates for definitive chemoradiation,” said Dr. Rizvi. “This gives doctors important new data when considering Libtayo for the varied patients and situations they treat in daily clinical practice.”
A version of this article first appeared on Medscape.com.
Organ transplant patient dies after receiving COVID-19–infected lungs
Doctors say a woman in Michigan contracted COVID-19 and died last fall 2 months after receiving a tainted double-lung transplant from a donor who turned out to harbor the virus that causes the disease – despite showing no signs of illness and initially testing negative.
Officials at the University of Michigan Medical School suggested it may be the first proven case of COVID-19 in the U.S. in which the virus was transmitted via an organ transplant. A surgeon who handled the donor lungs was also infected with the virus and fell ill but later recovered.
The incident appears to be isolated – the only confirmed case among nearly 40,000 transplants in 2020. But it has led to calls for more thorough testing of lung transplant donors, with samples taken from deep within the donor lungs as well as the nose and throat, said Dr. Daniel Kaul, director of Michigan Medicine’s transplant infectious disease service.
“We would absolutely not have used the lungs if we’d had a positive COVID-19 test,” said Dr. Kaul, who coauthored a report about the case in the American Journal of Transplantation.
The virus was transmitted when lungs from a woman from the Upper Midwest, who died after suffering a severe brain injury in a car accident, were transplanted into a woman with chronic obstructive lung disease at University Hospital in Ann Arbor. The nose and throat samples routinely collected from both organ donors and recipients tested negative for SARS-CoV-2, the virus that causes covid.
“All the screening that we normally do and are able to do, we did,” Dr. Kaul said.
Three days after the operation, however, the recipient spiked a fever; her blood pressure fell and her breathing became labored. Imaging showed signs of lung infection.
As her condition worsened, the patient developed septic shock and heart function problems. Doctors decided to test for SARS-CoV-2, Dr. Kaul said. Samples from her new lungs came back positive.
Suspicious about the origin of the infection, doctors returned to samples from the transplant donor. A molecular test of a swab from the donor’s nose and throat, taken 48 hours after her lungs were procured, had been negative for SARS-Cov-2. The donor’s family told doctors she had no history of recent travel or COVID-19 symptoms and no known exposure to anyone with the disease.
But doctors had kept a sample of fluid washed from deep within the donor lungs. When they tested that fluid, it was positive for the virus. Four days after the transplant, the surgeon who handled the donor lungs and performed the surgery tested positive, too. Genetic screening revealed that the transplant recipient and the surgeon had been infected by the donor. Ten other members of the transplant team tested negative for the virus.
The transplant recipient deteriorated rapidly, developing multisystem organ failure. Doctors tried known treatments for COVID-19, including remdesivir, a newly approved drug, and convalescent blood plasma from people previously infected with the disease. Eventually, she was placed on the last-resort option of ECMO, or extracorporeal membrane oxygenation, to no avail. Life support was withdrawn, and she died 61 days after the transplant.
Dr. Kaul called the incident “a tragic case.”
While the Michigan case marks the first confirmed incident in the U.S. of transmission through a transplant, others have been suspected. A recent Centers for Disease Control and Prevention report reviewed eight possible cases of what’s known as donor-derived infection that occurred last spring, but concluded the most likely source of transmission of the COVID-19 virus in those cases was in a community or health care setting.
Before this incident, it was not clear whether the COVID-19 virus could be transmitted through solid organ transplants, though it’s well documented with other respiratory viruses. Donor transmission of H1N1 2009 pandemic influenza has been detected almost exclusively in lung transplant recipients, Dr. Kaul noted.
While it’s not surprising that SARS-CoV-2 can be transmitted through infected lungs, it remains uncertain whether other organs affected by COVID-19 – hearts, livers and kidneys, for instance – can transmit the virus, too.
“It seems for non-lung donors that it may be very difficult to transmit COVID-19, even if the donor has COVID-19,” Dr. Kaul said.
Organ donors have been tested routinely for SARS-CoV-2 during the pandemic, though it’s not required by the Organ Procurement and Transplantation Network, or OPTN, which oversees transplants in the U.S. But the Michigan case underscores the need for more extensive sampling before transplant, especially in areas with high rates of covid transmission, Dr. Kaul said.
When it comes to lungs, that means making sure to test samples from the donor’s lower respiratory tract, as well as from the nose and throat. Obtaining and testing such samples from donors can be difficult to carry out in a timely fashion. There’s also the risk of introducing infection into the donated lungs, Dr. Kaul said.
Because no organs other than lungs were used, the Michigan case doesn’t provide insight into testing protocols for other organs.
Overall, viral transmissions from organ donors to recipients remain rare, occurring in fewer than 1% of transplant recipients, research shows. The medical risks facing ailing patients who reject a donor organ are generally far higher, said Dr. David Klassen, chief medical officer with the United Network for Organ Sharing, the federal contractor that runs the OPTN.
“The risks of turning down transplants are catastrophic,” he said. “I don’t think patients should be afraid of the transplant process.”
Kaiser Health News is a nonprofit news service covering health issues. It is an editorially independent program of KFF (Kaiser Family Foundation), which is not affiliated with Kaiser Permanente.
Doctors say a woman in Michigan contracted COVID-19 and died last fall 2 months after receiving a tainted double-lung transplant from a donor who turned out to harbor the virus that causes the disease – despite showing no signs of illness and initially testing negative.
Officials at the University of Michigan Medical School suggested it may be the first proven case of COVID-19 in the U.S. in which the virus was transmitted via an organ transplant. A surgeon who handled the donor lungs was also infected with the virus and fell ill but later recovered.
The incident appears to be isolated – the only confirmed case among nearly 40,000 transplants in 2020. But it has led to calls for more thorough testing of lung transplant donors, with samples taken from deep within the donor lungs as well as the nose and throat, said Dr. Daniel Kaul, director of Michigan Medicine’s transplant infectious disease service.
“We would absolutely not have used the lungs if we’d had a positive COVID-19 test,” said Dr. Kaul, who coauthored a report about the case in the American Journal of Transplantation.
The virus was transmitted when lungs from a woman from the Upper Midwest, who died after suffering a severe brain injury in a car accident, were transplanted into a woman with chronic obstructive lung disease at University Hospital in Ann Arbor. The nose and throat samples routinely collected from both organ donors and recipients tested negative for SARS-CoV-2, the virus that causes covid.
“All the screening that we normally do and are able to do, we did,” Dr. Kaul said.
Three days after the operation, however, the recipient spiked a fever; her blood pressure fell and her breathing became labored. Imaging showed signs of lung infection.
As her condition worsened, the patient developed septic shock and heart function problems. Doctors decided to test for SARS-CoV-2, Dr. Kaul said. Samples from her new lungs came back positive.
Suspicious about the origin of the infection, doctors returned to samples from the transplant donor. A molecular test of a swab from the donor’s nose and throat, taken 48 hours after her lungs were procured, had been negative for SARS-Cov-2. The donor’s family told doctors she had no history of recent travel or COVID-19 symptoms and no known exposure to anyone with the disease.
But doctors had kept a sample of fluid washed from deep within the donor lungs. When they tested that fluid, it was positive for the virus. Four days after the transplant, the surgeon who handled the donor lungs and performed the surgery tested positive, too. Genetic screening revealed that the transplant recipient and the surgeon had been infected by the donor. Ten other members of the transplant team tested negative for the virus.
The transplant recipient deteriorated rapidly, developing multisystem organ failure. Doctors tried known treatments for COVID-19, including remdesivir, a newly approved drug, and convalescent blood plasma from people previously infected with the disease. Eventually, she was placed on the last-resort option of ECMO, or extracorporeal membrane oxygenation, to no avail. Life support was withdrawn, and she died 61 days after the transplant.
Dr. Kaul called the incident “a tragic case.”
While the Michigan case marks the first confirmed incident in the U.S. of transmission through a transplant, others have been suspected. A recent Centers for Disease Control and Prevention report reviewed eight possible cases of what’s known as donor-derived infection that occurred last spring, but concluded the most likely source of transmission of the COVID-19 virus in those cases was in a community or health care setting.
Before this incident, it was not clear whether the COVID-19 virus could be transmitted through solid organ transplants, though it’s well documented with other respiratory viruses. Donor transmission of H1N1 2009 pandemic influenza has been detected almost exclusively in lung transplant recipients, Dr. Kaul noted.
While it’s not surprising that SARS-CoV-2 can be transmitted through infected lungs, it remains uncertain whether other organs affected by COVID-19 – hearts, livers and kidneys, for instance – can transmit the virus, too.
“It seems for non-lung donors that it may be very difficult to transmit COVID-19, even if the donor has COVID-19,” Dr. Kaul said.
Organ donors have been tested routinely for SARS-CoV-2 during the pandemic, though it’s not required by the Organ Procurement and Transplantation Network, or OPTN, which oversees transplants in the U.S. But the Michigan case underscores the need for more extensive sampling before transplant, especially in areas with high rates of covid transmission, Dr. Kaul said.
When it comes to lungs, that means making sure to test samples from the donor’s lower respiratory tract, as well as from the nose and throat. Obtaining and testing such samples from donors can be difficult to carry out in a timely fashion. There’s also the risk of introducing infection into the donated lungs, Dr. Kaul said.
Because no organs other than lungs were used, the Michigan case doesn’t provide insight into testing protocols for other organs.
Overall, viral transmissions from organ donors to recipients remain rare, occurring in fewer than 1% of transplant recipients, research shows. The medical risks facing ailing patients who reject a donor organ are generally far higher, said Dr. David Klassen, chief medical officer with the United Network for Organ Sharing, the federal contractor that runs the OPTN.
“The risks of turning down transplants are catastrophic,” he said. “I don’t think patients should be afraid of the transplant process.”
Kaiser Health News is a nonprofit news service covering health issues. It is an editorially independent program of KFF (Kaiser Family Foundation), which is not affiliated with Kaiser Permanente.
Doctors say a woman in Michigan contracted COVID-19 and died last fall 2 months after receiving a tainted double-lung transplant from a donor who turned out to harbor the virus that causes the disease – despite showing no signs of illness and initially testing negative.
Officials at the University of Michigan Medical School suggested it may be the first proven case of COVID-19 in the U.S. in which the virus was transmitted via an organ transplant. A surgeon who handled the donor lungs was also infected with the virus and fell ill but later recovered.
The incident appears to be isolated – the only confirmed case among nearly 40,000 transplants in 2020. But it has led to calls for more thorough testing of lung transplant donors, with samples taken from deep within the donor lungs as well as the nose and throat, said Dr. Daniel Kaul, director of Michigan Medicine’s transplant infectious disease service.
“We would absolutely not have used the lungs if we’d had a positive COVID-19 test,” said Dr. Kaul, who coauthored a report about the case in the American Journal of Transplantation.
The virus was transmitted when lungs from a woman from the Upper Midwest, who died after suffering a severe brain injury in a car accident, were transplanted into a woman with chronic obstructive lung disease at University Hospital in Ann Arbor. The nose and throat samples routinely collected from both organ donors and recipients tested negative for SARS-CoV-2, the virus that causes covid.
“All the screening that we normally do and are able to do, we did,” Dr. Kaul said.
Three days after the operation, however, the recipient spiked a fever; her blood pressure fell and her breathing became labored. Imaging showed signs of lung infection.
As her condition worsened, the patient developed septic shock and heart function problems. Doctors decided to test for SARS-CoV-2, Dr. Kaul said. Samples from her new lungs came back positive.
Suspicious about the origin of the infection, doctors returned to samples from the transplant donor. A molecular test of a swab from the donor’s nose and throat, taken 48 hours after her lungs were procured, had been negative for SARS-Cov-2. The donor’s family told doctors she had no history of recent travel or COVID-19 symptoms and no known exposure to anyone with the disease.
But doctors had kept a sample of fluid washed from deep within the donor lungs. When they tested that fluid, it was positive for the virus. Four days after the transplant, the surgeon who handled the donor lungs and performed the surgery tested positive, too. Genetic screening revealed that the transplant recipient and the surgeon had been infected by the donor. Ten other members of the transplant team tested negative for the virus.
The transplant recipient deteriorated rapidly, developing multisystem organ failure. Doctors tried known treatments for COVID-19, including remdesivir, a newly approved drug, and convalescent blood plasma from people previously infected with the disease. Eventually, she was placed on the last-resort option of ECMO, or extracorporeal membrane oxygenation, to no avail. Life support was withdrawn, and she died 61 days after the transplant.
Dr. Kaul called the incident “a tragic case.”
While the Michigan case marks the first confirmed incident in the U.S. of transmission through a transplant, others have been suspected. A recent Centers for Disease Control and Prevention report reviewed eight possible cases of what’s known as donor-derived infection that occurred last spring, but concluded the most likely source of transmission of the COVID-19 virus in those cases was in a community or health care setting.
Before this incident, it was not clear whether the COVID-19 virus could be transmitted through solid organ transplants, though it’s well documented with other respiratory viruses. Donor transmission of H1N1 2009 pandemic influenza has been detected almost exclusively in lung transplant recipients, Dr. Kaul noted.
While it’s not surprising that SARS-CoV-2 can be transmitted through infected lungs, it remains uncertain whether other organs affected by COVID-19 – hearts, livers and kidneys, for instance – can transmit the virus, too.
“It seems for non-lung donors that it may be very difficult to transmit COVID-19, even if the donor has COVID-19,” Dr. Kaul said.
Organ donors have been tested routinely for SARS-CoV-2 during the pandemic, though it’s not required by the Organ Procurement and Transplantation Network, or OPTN, which oversees transplants in the U.S. But the Michigan case underscores the need for more extensive sampling before transplant, especially in areas with high rates of covid transmission, Dr. Kaul said.
When it comes to lungs, that means making sure to test samples from the donor’s lower respiratory tract, as well as from the nose and throat. Obtaining and testing such samples from donors can be difficult to carry out in a timely fashion. There’s also the risk of introducing infection into the donated lungs, Dr. Kaul said.
Because no organs other than lungs were used, the Michigan case doesn’t provide insight into testing protocols for other organs.
Overall, viral transmissions from organ donors to recipients remain rare, occurring in fewer than 1% of transplant recipients, research shows. The medical risks facing ailing patients who reject a donor organ are generally far higher, said Dr. David Klassen, chief medical officer with the United Network for Organ Sharing, the federal contractor that runs the OPTN.
“The risks of turning down transplants are catastrophic,” he said. “I don’t think patients should be afraid of the transplant process.”
Kaiser Health News is a nonprofit news service covering health issues. It is an editorially independent program of KFF (Kaiser Family Foundation), which is not affiliated with Kaiser Permanente.
How has the pandemic affected rural and urban cancer patients?
Research has shown that, compared with their urban counterparts, rural cancer patients have higher cancer-related mortality and other negative treatment outcomes.
Among other explanations, the disparity has been attributed to lower education and income levels, medical and behavioral risk factors, differences in health literacy, and lower confidence in the medical system among rural residents (JCO Oncol Pract. 2020 Jul;16(7):422-30).
A new survey has provided some insight into how the COVID-19 pandemic has impacted rural and urban cancer patients differently.
The survey showed that urban patients were more likely to report changes to their daily lives, thought themselves more likely to become infected with SARS-CoV-2, and were more likely to take measures to mitigate the risk of infection. However, there were no major differences between urban and rural patients with regard to changes in social interaction.
Bailee Daniels of the University of Utah in Salt Lake City, presented these results at the AACR Virtual Meeting: COVID-19 and Cancer (Abstract S04-03).
The COVID-19 and Oncology Patient Experience Consortium
Ms. Daniels explained that the COVID-19 and Oncology Patient Experience (COPES) Consortium was created to investigate various aspects of the patient experience during the pandemic. Three cancer centers – Moffitt Cancer Center, Huntsman Cancer Institute, and the Sylvester Comprehensive Cancer Center – participate in COPES.
At Huntsman, investigators studied social and health behaviors of cancer patients to assess whether there was a difference between those from rural and urban areas. The researchers looked at the impact of the pandemic on psychosocial outcomes, preventive measures patients implemented, and their perceptions of the risk of SARS-CoV-2 infection.
The team’s hypothesis was that rural patients might be more vulnerable than urban patients to the effects of social isolation, emotional distress, and health-adverse behaviors, but the investigators noted that there has been no prior research on the topic.
Assessing behaviors, attitudes, and outcomes
Between August and September 2020, the researchers surveyed 1,328 adult cancer patients who had visited Huntsman in the previous 4 years and who were enrolled in Huntsman’s Total Cancer Care or Precision Exercise Prescription studies.
Patients completed questionnaires that encompassed demographic and clinical factors, employment status, health behaviors, and infection preventive measures. Questionnaires were provided in electronic, paper, or phone-based formats. Information regarding age, race, ethnicity, and tumor stage was abstracted from Huntsman’s electronic health record.
Modifications in daily life and social interaction were assessed on a 5-point scale. Changes in exercise habits and alcohol consumption were assessed on a 3-point scale. Infection mitigation measures (the use of face masks and hand sanitizer) and perceptions about the likelihood of SARS-CoV-2 infection were measured.
The rural-urban community area codes system, which classifies U.S. census tracts by measures of population density, urbanization, and daily commuting, was utilized to categorize patients into rural and urban residences.
Characteristics of urban and rural cancer patients
There were 997 urban and 331 rural participants. The mean age was 60.1 years in the urban population and 62.6 years in the rural population (P = .01). There were no urban-rural differences in sex, ethnicity, cancer stage, or body mass index.
More urban than rural participants were employed full- or part-time (45% vs. 37%; P = .045). The rural counties had more patients who were not currently employed, primarily due to retirement (77% vs. 69% urban; P < .001).
“No health insurance coverage” was reported by 2% of urban and 4% of rural participants (P = .009), and 85% of all patients reported “good” to “excellent” overall health. Cancer patients in rural counties were significantly more likely to have ever smoked (37% vs. 25% urban; P = .001). In addition, alcohol consumption in the previous year was higher in rural patients. “Every day to less than once monthly” alcohol usage was reported by 44% of urban and 60% of rural patients (P < .001).
Changes in daily life and health-related behavior during the pandemic
Urban patients were more likely to report changes in their daily lives due to the pandemic. Specifically, 35% of urban patients and 26% of rural patients said the pandemic had changed their daily life “a lot” (P = .001).
However, there were no major differences between urban and rural patients when it came to changes in social interaction in the past month or feeling lonely in the past month (P = .45 and P = .88, respectively). Similarly, there were no significant differences for changes in alcohol consumption between the groups (P = .90).
Changes in exercise habits due to the pandemic were more common among patients in urban counties (51% vs. 39% rural; P < .001), though similar percentages of patients reported exercising less (44% urban vs. 45% rural) or more frequently (24% urban vs. 20% rural).
In terms of infection mitigation measures, urban patients were more likely to use face masks “very often” (83% vs. 66% rural; P < .001), while hand sanitizer was used “very often” among 66% of urban and 57% of rural participants (P = .05).
Urban participants were more likely than were their rural counterparts to think themselves “somewhat” or “very” likely to develop COVID-19 (22% vs. 14%; P = .04).
It might be short-sighted for oncology and public health specialists to be dismissive of differences in infection mitigation behaviors and perceptions of vulnerability to SARS-CoV-2 infection. Those behaviors and perceptions of risk could lead to lower vaccination rates in rural areas. If that occurs, there would be major negative consequences for the long-term health of rural communities and their medically vulnerable residents.
Future directions
Although the first 6 months of the COVID-19 pandemic had disparate effects on cancer patients living in rural and urban counties, the reasons for the disparities are complex and not easily explained by this study.
It is possible that sequential administration of the survey during the pandemic would have uncovered greater variances in attitude and health-related behaviors.
As Ms. Daniels noted, when the survey was performed, Utah had not experienced a high frequency of COVID-19 cases. Furthermore, different levels of restrictions were implemented on a county-by-county basis, potentially influencing patients’ behaviors, psychosocial adjustment, and perceptions of risk.
In addition, there may have been differences in unmeasured endpoints (infection rates, medical care utilization via telemedicine, hospitalization rates, late effects, and mortality) between the urban and rural populations.
As the investigators concluded, further research is needed to better characterize the pandemic’s short- and long-term effects on cancer patients in rural and urban settings and appropriate interventions. Such studies may yield insights into the various facets of the well-documented “rural health gap” in cancer outcomes and interventions that could narrow the gap in spheres beyond the COVID-19 pandemic.
Ms. Daniels reported having no relevant disclosures.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
Research has shown that, compared with their urban counterparts, rural cancer patients have higher cancer-related mortality and other negative treatment outcomes.
Among other explanations, the disparity has been attributed to lower education and income levels, medical and behavioral risk factors, differences in health literacy, and lower confidence in the medical system among rural residents (JCO Oncol Pract. 2020 Jul;16(7):422-30).
A new survey has provided some insight into how the COVID-19 pandemic has impacted rural and urban cancer patients differently.
The survey showed that urban patients were more likely to report changes to their daily lives, thought themselves more likely to become infected with SARS-CoV-2, and were more likely to take measures to mitigate the risk of infection. However, there were no major differences between urban and rural patients with regard to changes in social interaction.
Bailee Daniels of the University of Utah in Salt Lake City, presented these results at the AACR Virtual Meeting: COVID-19 and Cancer (Abstract S04-03).
The COVID-19 and Oncology Patient Experience Consortium
Ms. Daniels explained that the COVID-19 and Oncology Patient Experience (COPES) Consortium was created to investigate various aspects of the patient experience during the pandemic. Three cancer centers – Moffitt Cancer Center, Huntsman Cancer Institute, and the Sylvester Comprehensive Cancer Center – participate in COPES.
At Huntsman, investigators studied social and health behaviors of cancer patients to assess whether there was a difference between those from rural and urban areas. The researchers looked at the impact of the pandemic on psychosocial outcomes, preventive measures patients implemented, and their perceptions of the risk of SARS-CoV-2 infection.
The team’s hypothesis was that rural patients might be more vulnerable than urban patients to the effects of social isolation, emotional distress, and health-adverse behaviors, but the investigators noted that there has been no prior research on the topic.
Assessing behaviors, attitudes, and outcomes
Between August and September 2020, the researchers surveyed 1,328 adult cancer patients who had visited Huntsman in the previous 4 years and who were enrolled in Huntsman’s Total Cancer Care or Precision Exercise Prescription studies.
Patients completed questionnaires that encompassed demographic and clinical factors, employment status, health behaviors, and infection preventive measures. Questionnaires were provided in electronic, paper, or phone-based formats. Information regarding age, race, ethnicity, and tumor stage was abstracted from Huntsman’s electronic health record.
Modifications in daily life and social interaction were assessed on a 5-point scale. Changes in exercise habits and alcohol consumption were assessed on a 3-point scale. Infection mitigation measures (the use of face masks and hand sanitizer) and perceptions about the likelihood of SARS-CoV-2 infection were measured.
The rural-urban community area codes system, which classifies U.S. census tracts by measures of population density, urbanization, and daily commuting, was utilized to categorize patients into rural and urban residences.
Characteristics of urban and rural cancer patients
There were 997 urban and 331 rural participants. The mean age was 60.1 years in the urban population and 62.6 years in the rural population (P = .01). There were no urban-rural differences in sex, ethnicity, cancer stage, or body mass index.
More urban than rural participants were employed full- or part-time (45% vs. 37%; P = .045). The rural counties had more patients who were not currently employed, primarily due to retirement (77% vs. 69% urban; P < .001).
“No health insurance coverage” was reported by 2% of urban and 4% of rural participants (P = .009), and 85% of all patients reported “good” to “excellent” overall health. Cancer patients in rural counties were significantly more likely to have ever smoked (37% vs. 25% urban; P = .001). In addition, alcohol consumption in the previous year was higher in rural patients. “Every day to less than once monthly” alcohol usage was reported by 44% of urban and 60% of rural patients (P < .001).
Changes in daily life and health-related behavior during the pandemic
Urban patients were more likely to report changes in their daily lives due to the pandemic. Specifically, 35% of urban patients and 26% of rural patients said the pandemic had changed their daily life “a lot” (P = .001).
However, there were no major differences between urban and rural patients when it came to changes in social interaction in the past month or feeling lonely in the past month (P = .45 and P = .88, respectively). Similarly, there were no significant differences for changes in alcohol consumption between the groups (P = .90).
Changes in exercise habits due to the pandemic were more common among patients in urban counties (51% vs. 39% rural; P < .001), though similar percentages of patients reported exercising less (44% urban vs. 45% rural) or more frequently (24% urban vs. 20% rural).
In terms of infection mitigation measures, urban patients were more likely to use face masks “very often” (83% vs. 66% rural; P < .001), while hand sanitizer was used “very often” among 66% of urban and 57% of rural participants (P = .05).
Urban participants were more likely than were their rural counterparts to think themselves “somewhat” or “very” likely to develop COVID-19 (22% vs. 14%; P = .04).
It might be short-sighted for oncology and public health specialists to be dismissive of differences in infection mitigation behaviors and perceptions of vulnerability to SARS-CoV-2 infection. Those behaviors and perceptions of risk could lead to lower vaccination rates in rural areas. If that occurs, there would be major negative consequences for the long-term health of rural communities and their medically vulnerable residents.
Future directions
Although the first 6 months of the COVID-19 pandemic had disparate effects on cancer patients living in rural and urban counties, the reasons for the disparities are complex and not easily explained by this study.
It is possible that sequential administration of the survey during the pandemic would have uncovered greater variances in attitude and health-related behaviors.
As Ms. Daniels noted, when the survey was performed, Utah had not experienced a high frequency of COVID-19 cases. Furthermore, different levels of restrictions were implemented on a county-by-county basis, potentially influencing patients’ behaviors, psychosocial adjustment, and perceptions of risk.
In addition, there may have been differences in unmeasured endpoints (infection rates, medical care utilization via telemedicine, hospitalization rates, late effects, and mortality) between the urban and rural populations.
As the investigators concluded, further research is needed to better characterize the pandemic’s short- and long-term effects on cancer patients in rural and urban settings and appropriate interventions. Such studies may yield insights into the various facets of the well-documented “rural health gap” in cancer outcomes and interventions that could narrow the gap in spheres beyond the COVID-19 pandemic.
Ms. Daniels reported having no relevant disclosures.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
Research has shown that, compared with their urban counterparts, rural cancer patients have higher cancer-related mortality and other negative treatment outcomes.
Among other explanations, the disparity has been attributed to lower education and income levels, medical and behavioral risk factors, differences in health literacy, and lower confidence in the medical system among rural residents (JCO Oncol Pract. 2020 Jul;16(7):422-30).
A new survey has provided some insight into how the COVID-19 pandemic has impacted rural and urban cancer patients differently.
The survey showed that urban patients were more likely to report changes to their daily lives, thought themselves more likely to become infected with SARS-CoV-2, and were more likely to take measures to mitigate the risk of infection. However, there were no major differences between urban and rural patients with regard to changes in social interaction.
Bailee Daniels of the University of Utah in Salt Lake City, presented these results at the AACR Virtual Meeting: COVID-19 and Cancer (Abstract S04-03).
The COVID-19 and Oncology Patient Experience Consortium
Ms. Daniels explained that the COVID-19 and Oncology Patient Experience (COPES) Consortium was created to investigate various aspects of the patient experience during the pandemic. Three cancer centers – Moffitt Cancer Center, Huntsman Cancer Institute, and the Sylvester Comprehensive Cancer Center – participate in COPES.
At Huntsman, investigators studied social and health behaviors of cancer patients to assess whether there was a difference between those from rural and urban areas. The researchers looked at the impact of the pandemic on psychosocial outcomes, preventive measures patients implemented, and their perceptions of the risk of SARS-CoV-2 infection.
The team’s hypothesis was that rural patients might be more vulnerable than urban patients to the effects of social isolation, emotional distress, and health-adverse behaviors, but the investigators noted that there has been no prior research on the topic.
Assessing behaviors, attitudes, and outcomes
Between August and September 2020, the researchers surveyed 1,328 adult cancer patients who had visited Huntsman in the previous 4 years and who were enrolled in Huntsman’s Total Cancer Care or Precision Exercise Prescription studies.
Patients completed questionnaires that encompassed demographic and clinical factors, employment status, health behaviors, and infection preventive measures. Questionnaires were provided in electronic, paper, or phone-based formats. Information regarding age, race, ethnicity, and tumor stage was abstracted from Huntsman’s electronic health record.
Modifications in daily life and social interaction were assessed on a 5-point scale. Changes in exercise habits and alcohol consumption were assessed on a 3-point scale. Infection mitigation measures (the use of face masks and hand sanitizer) and perceptions about the likelihood of SARS-CoV-2 infection were measured.
The rural-urban community area codes system, which classifies U.S. census tracts by measures of population density, urbanization, and daily commuting, was utilized to categorize patients into rural and urban residences.
Characteristics of urban and rural cancer patients
There were 997 urban and 331 rural participants. The mean age was 60.1 years in the urban population and 62.6 years in the rural population (P = .01). There were no urban-rural differences in sex, ethnicity, cancer stage, or body mass index.
More urban than rural participants were employed full- or part-time (45% vs. 37%; P = .045). The rural counties had more patients who were not currently employed, primarily due to retirement (77% vs. 69% urban; P < .001).
“No health insurance coverage” was reported by 2% of urban and 4% of rural participants (P = .009), and 85% of all patients reported “good” to “excellent” overall health. Cancer patients in rural counties were significantly more likely to have ever smoked (37% vs. 25% urban; P = .001). In addition, alcohol consumption in the previous year was higher in rural patients. “Every day to less than once monthly” alcohol usage was reported by 44% of urban and 60% of rural patients (P < .001).
Changes in daily life and health-related behavior during the pandemic
Urban patients were more likely to report changes in their daily lives due to the pandemic. Specifically, 35% of urban patients and 26% of rural patients said the pandemic had changed their daily life “a lot” (P = .001).
However, there were no major differences between urban and rural patients when it came to changes in social interaction in the past month or feeling lonely in the past month (P = .45 and P = .88, respectively). Similarly, there were no significant differences for changes in alcohol consumption between the groups (P = .90).
Changes in exercise habits due to the pandemic were more common among patients in urban counties (51% vs. 39% rural; P < .001), though similar percentages of patients reported exercising less (44% urban vs. 45% rural) or more frequently (24% urban vs. 20% rural).
In terms of infection mitigation measures, urban patients were more likely to use face masks “very often” (83% vs. 66% rural; P < .001), while hand sanitizer was used “very often” among 66% of urban and 57% of rural participants (P = .05).
Urban participants were more likely than were their rural counterparts to think themselves “somewhat” or “very” likely to develop COVID-19 (22% vs. 14%; P = .04).
It might be short-sighted for oncology and public health specialists to be dismissive of differences in infection mitigation behaviors and perceptions of vulnerability to SARS-CoV-2 infection. Those behaviors and perceptions of risk could lead to lower vaccination rates in rural areas. If that occurs, there would be major negative consequences for the long-term health of rural communities and their medically vulnerable residents.
Future directions
Although the first 6 months of the COVID-19 pandemic had disparate effects on cancer patients living in rural and urban counties, the reasons for the disparities are complex and not easily explained by this study.
It is possible that sequential administration of the survey during the pandemic would have uncovered greater variances in attitude and health-related behaviors.
As Ms. Daniels noted, when the survey was performed, Utah had not experienced a high frequency of COVID-19 cases. Furthermore, different levels of restrictions were implemented on a county-by-county basis, potentially influencing patients’ behaviors, psychosocial adjustment, and perceptions of risk.
In addition, there may have been differences in unmeasured endpoints (infection rates, medical care utilization via telemedicine, hospitalization rates, late effects, and mortality) between the urban and rural populations.
As the investigators concluded, further research is needed to better characterize the pandemic’s short- and long-term effects on cancer patients in rural and urban settings and appropriate interventions. Such studies may yield insights into the various facets of the well-documented “rural health gap” in cancer outcomes and interventions that could narrow the gap in spheres beyond the COVID-19 pandemic.
Ms. Daniels reported having no relevant disclosures.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
FROM AACR: COVID-19 AND CANCER 2021
X-ray vision: Using AI to maximize the value of radiographic images
Artificial intelligence (AI) is expected to one day affect the entire continuum of cancer care – from screening and risk prediction to diagnosis, risk stratification, treatment selection, and follow-up, according to an expert in the field.
Hugo J.W.L. Aerts, PhD, director of the AI in Medicine Program at Brigham and Women’s Hospital in Boston, described studies using AI for some of these purposes during a presentation at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (Abstract IA-06).
In one study, Dr. Aerts and colleagues set out to determine whether a convolutional neural network (CNN) could extract prognostic information from chest radiographs. The researchers tested this theory using patients from two trials – the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and the National Lung Screening Trial (NLST).
The team developed a CNN, called CXR-risk, and tested whether it could predict the longevity and prognosis of patients in the PLCO (n = 52,320) and NLST (n = 5,493) trials over a 12-year time period, based only on chest radiographs. No clinical information, demographics, radiographic interpretations, duration of follow-up, or censoring were provided to the deep-learning system.
CXR-risk output was stratified into five categories of radiographic risk scores for probability of death, from 0 (very low likelihood of mortality) to 1 (very high likelihood of mortality).
The investigators found a graded association between radiographic risk score and mortality. The very-high-risk group had mortality rates of 53.0% (PLCO) and 33.9% (NLST). In both trials, this was significantly higher than for the very-low-risk group. The unadjusted hazard ratio was 18.3 in the PCLO data set and 15.2 in the NLST data set (P < .001 for both).
This association was maintained after adjustment for radiologists’ findings (e.g., a lung nodule) and risk factors such as age, gender, and comorbid illnesses like diabetes. The adjusted HR was 4.8 in the PCLO data set and 7.0 in the NLST data set (P < .001 for both).
In both data sets, individuals in the very-high-risk group were significantly more likely to die of lung cancer. The aHR was 11.1 in the PCLO data set and 8.4 in the NSLT data set (P < .001 for both).
This might be expected for people who were interested in being screened for lung cancer. However, patients in the very-high-risk group were also more likely to die of cardiovascular illness (aHR, 3.6 for PLCO and 47.8 for NSLT; P < .001 for both) and respiratory illness (aHR, 27.5 for PLCO and 31.9 for NLST; P ≤ .001 for both).
With this information, a clinician could initiate additional testing and/or utilize more aggressive surveillance measures. If an oncologist considered therapy for a patient with newly diagnosed cancer, treatment choices and stratification for adverse events would be more intelligently planned.
Using AI to predict the risk of lung cancer
In another study, Dr. Aerts and colleagues developed and validated a CNN called CXR-LC, which was based on CXR-risk. The goal of this study was to see if CXR-LC could predict long-term incident lung cancer using data available in the EHR, including chest radiographs, age, sex, and smoking status.
The CXR-LC model was developed using data from the PLCO trial (n = 41,856) and was validated in smokers from the PLCO trial (n = 5,615; 12-year follow-up) as well as heavy smokers from the NLST trial (n = 5,493; 6-year follow-up).
Results showed that CXR-LC was able to predict which patients were at highest risk for developing lung cancer.
CXR-LC had better discrimination for incident lung cancer than did Medicare eligibility in the PLCO data set (area under the curve, 0.755 vs. 0.634; P < .001). And the performance of CXR-LC was similar to that of the PLCOM2012 risk score in both the PLCO data set (AUC, 0.755 vs. 0.751) and the NLST data set (AUC, 0.659 vs. 0.650).
When they were compared in screening populations of equal size, CXR-LC was more sensitive than Medicare eligibility criteria in the PLCO data set (74.9% vs. 63.8%; P = .012) and missed 30.7% fewer incident lung cancer diagnoses.
AI as a substitute for specialized testing and consultation
In a third study, Dr. Aerts and colleagues used a CNN to predict cardiovascular risk by assessing coronary artery calcium (CAC) from clinically obtained, readily available CT scans.
Ordinarily, identifying CAC – an accurate predictor of cardiovascular events – requires specialized expertise (manual measurement and cardiologist interpretation), time (estimated at 20 minutes/scan), and equipment (ECG-gated cardiac CT scan and special software).
In this study, the researchers used a fully end-to-end automated system with analytic time measured in less than 2 seconds.
The team trained and tuned their CNN using the Framingham Heart Study Offspring and Third Generation cohorts (n = 1,636), which included asymptomatic patients with high-quality, cardiac-gated CT scans for CAC quantification.
The researchers then tested the CNN on two asymptomatic and two symptomatic cohorts:
- Asymptomatic Framingham Heart Study participants (n = 663) in whom the outcome measures were cardiovascular disease and death.
- Asymptomatic NLST participants (n = 14,959) in whom the outcome measure was atherosclerotic cardiovascular death.
- Symptomatic PROMISE study participants with stable chest pain (n = 4,021) in whom the outcome measures were all-cause mortality, MI, and hospitalization for unstable angina.
- Symptomatic ROMICAT-II study patients with acute chest pain (n = 441) in whom the outcome measure was acute coronary syndrome at 28 days.
Among 5,521 subjects across all testing cohorts with cardiac-gated and nongated chest CT scans, the CNN and expert reader interpretations agreed on the CAC risk scores with a high level of concordance (kappa, 0.71; concordance rate, 0.79).
There was a very high Spearman’s correlation of 0.92 (P < .0001) and substantial agreement between automatically and manually calculated CAC risk groups, substantiating robust risk prediction for cardiovascular disease across multiple clinical scenarios.
Dr. Aerts commented that, among the NLST participants who had the highest risk of developing lung cancer, the risk of cardiovascular death was as high as the risk of death from lung cancer.
Using AI to assess patient outcomes
In an unpublished study, Dr. Aerts and colleagues used AI in an attempt to determine whether changes in measurements of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle mass would provide clues about treatment outcomes in lung cancer patients.
The researchers developed a deep learning model using data from 1,129 patients at Massachusetts General and Brigham and Women’s Hospitals, measuring SAT, VAT, and muscle mass. The team applied the measurement system to a population of 12,128 outpatients and calculated z scores for SAT, VAT, and muscle mass to determine “normal” values.
When they applied the norms to surgical lung cancer data sets from the Boston Lung Cancer Study (n = 437) and TRACERx study (n = 394), the researchers found that smokers had lower adiposity and lower muscle mass than never-smokers.
More importantly, over time, among lung cancer patients who lost greater than 5% of VAT, SAT, and muscle mass, those patients with the greatest SAT loss (P < .0001) or VAT loss (P = .0015) had the lowest lung cancer–specific survival in the TRACERx study. There was no significant impairment of lung cancer-specific survival for patients who experienced skeletal muscle loss (P = .23).
The same observation was made for overall survival among patients enrolled in the Boston Lung Cancer Study, using the 5% threshold. Overall survival was significantly worse with increasing VAT loss (P = .0023) and SAT loss (P = .0082) but not with increasing skeletal muscle loss (P = .3).
The investigators speculated about whether the correlation between body composition and clinical outcome could yield clues about tumor biology. To test this, the researchers used the RNA sequencing–based ORACLE risk score in lung cancer patients from TRACERx. There was a high correlation between higher ORACLE risk scores and lower VAT and SAT, suggesting that measures of adiposity on CT were reflected in tumor biology patterns on an RNA level in lung cancer patients. There was no such correlation between ORACLE risk scores and skeletal muscle mass.
Wonderment ... tempered by concern and challenges
AI has awe-inspiring potential to yield actionable and prognostically important information from data mining the EHR and extracting the vast quantities of information from images. In some cases (like CAC), it is information that is “hiding in plain sight.” However, Dr. Aerts expressed several cautions, some of which have already plagued AI.
He referenced the Gartner Hype Cycle, which provides a graphic representation of five phases in the life cycle of emerging technologies. The “innovation trigger” is followed by a “peak of inflated expectations,” a “trough of disillusionment,” a “slope of enlightenment,” and a “plateau of productivity.”
Dr. Aerts noted that, in recent years, AI has seemed to fall into the trough of disillusionment, but it may be entering the slope of enlightenment on the way to the plateau of productivity.
His research highlighted several examples of productivity in radiomics in cancer patients and those who are at high risk of developing cancer.
In Dr. Aerts’s opinion, a second concern is replication of AI research results. He noted that, among 400 published studies, only 6% of authors shared the codes that would enable their findings to be corroborated. About 30% shared test data, and 54% shared “pseudocodes,” but transparency and reproducibility are problems for the acceptance and broad implementation of AI.
Dr. Aerts endorsed the Modelhub initiative (www.modelhub.ai), a multi-institutional initiative to advance reproducibility in the AI field and advance its full potential.
However, there are additional concerns about the implementation of radiomics and, more generally, data mining from clinicians’ EHRs to personalize care.
Firstly, it may be laborious and difficult to explain complex, computer-based risk stratification models to patients. Hereditary cancer testing is an example of a risk assessment test that requires complicated explanations that many clinicians relegate to genetics counselors – when patients elect to see them. When a model is not explainable, it undermines the confidence of patients and their care providers, according to an editorial related to the CXR-LC study.
Another issue is that uptake of lung cancer screening, in practice, has been underutilized by individuals who meet current, relatively straightforward Medicare criteria. Despite the apparently better accuracy of the CXR-LC deep-learning model, its complexity and limited access could constitute an additional barrier for the at-risk individuals who should avail themselves of screening.
Furthermore, although age and gender are accurate in most circumstances, there is legitimate concern about the accuracy of, for example, smoking history data and comorbid conditions in current EHRs. Who performs the laborious curation of the input in an AI model to assure its accuracy for individual patients?
Finally, it is unclear how scalable and applicable AI will be to medically underserved populations (e.g., smaller, community-based, free-standing, socioeconomically disadvantaged or rural health care institutions). There are substantial initial and maintenance costs that may limit AI’s availability to some academic institutions and large health maintenance organizations.
As the concerns and challenges are addressed, it will be interesting to see where and when the plateau of productivity for AI in cancer care occurs. When it does, many cancer patients will benefit from enhanced care along the continuum of the complex disease they and their caregivers seek to master.
Dr. Aerts disclosed relationships with Onc.AI outside the presented work.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
Artificial intelligence (AI) is expected to one day affect the entire continuum of cancer care – from screening and risk prediction to diagnosis, risk stratification, treatment selection, and follow-up, according to an expert in the field.
Hugo J.W.L. Aerts, PhD, director of the AI in Medicine Program at Brigham and Women’s Hospital in Boston, described studies using AI for some of these purposes during a presentation at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (Abstract IA-06).
In one study, Dr. Aerts and colleagues set out to determine whether a convolutional neural network (CNN) could extract prognostic information from chest radiographs. The researchers tested this theory using patients from two trials – the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and the National Lung Screening Trial (NLST).
The team developed a CNN, called CXR-risk, and tested whether it could predict the longevity and prognosis of patients in the PLCO (n = 52,320) and NLST (n = 5,493) trials over a 12-year time period, based only on chest radiographs. No clinical information, demographics, radiographic interpretations, duration of follow-up, or censoring were provided to the deep-learning system.
CXR-risk output was stratified into five categories of radiographic risk scores for probability of death, from 0 (very low likelihood of mortality) to 1 (very high likelihood of mortality).
The investigators found a graded association between radiographic risk score and mortality. The very-high-risk group had mortality rates of 53.0% (PLCO) and 33.9% (NLST). In both trials, this was significantly higher than for the very-low-risk group. The unadjusted hazard ratio was 18.3 in the PCLO data set and 15.2 in the NLST data set (P < .001 for both).
This association was maintained after adjustment for radiologists’ findings (e.g., a lung nodule) and risk factors such as age, gender, and comorbid illnesses like diabetes. The adjusted HR was 4.8 in the PCLO data set and 7.0 in the NLST data set (P < .001 for both).
In both data sets, individuals in the very-high-risk group were significantly more likely to die of lung cancer. The aHR was 11.1 in the PCLO data set and 8.4 in the NSLT data set (P < .001 for both).
This might be expected for people who were interested in being screened for lung cancer. However, patients in the very-high-risk group were also more likely to die of cardiovascular illness (aHR, 3.6 for PLCO and 47.8 for NSLT; P < .001 for both) and respiratory illness (aHR, 27.5 for PLCO and 31.9 for NLST; P ≤ .001 for both).
With this information, a clinician could initiate additional testing and/or utilize more aggressive surveillance measures. If an oncologist considered therapy for a patient with newly diagnosed cancer, treatment choices and stratification for adverse events would be more intelligently planned.
Using AI to predict the risk of lung cancer
In another study, Dr. Aerts and colleagues developed and validated a CNN called CXR-LC, which was based on CXR-risk. The goal of this study was to see if CXR-LC could predict long-term incident lung cancer using data available in the EHR, including chest radiographs, age, sex, and smoking status.
The CXR-LC model was developed using data from the PLCO trial (n = 41,856) and was validated in smokers from the PLCO trial (n = 5,615; 12-year follow-up) as well as heavy smokers from the NLST trial (n = 5,493; 6-year follow-up).
Results showed that CXR-LC was able to predict which patients were at highest risk for developing lung cancer.
CXR-LC had better discrimination for incident lung cancer than did Medicare eligibility in the PLCO data set (area under the curve, 0.755 vs. 0.634; P < .001). And the performance of CXR-LC was similar to that of the PLCOM2012 risk score in both the PLCO data set (AUC, 0.755 vs. 0.751) and the NLST data set (AUC, 0.659 vs. 0.650).
When they were compared in screening populations of equal size, CXR-LC was more sensitive than Medicare eligibility criteria in the PLCO data set (74.9% vs. 63.8%; P = .012) and missed 30.7% fewer incident lung cancer diagnoses.
AI as a substitute for specialized testing and consultation
In a third study, Dr. Aerts and colleagues used a CNN to predict cardiovascular risk by assessing coronary artery calcium (CAC) from clinically obtained, readily available CT scans.
Ordinarily, identifying CAC – an accurate predictor of cardiovascular events – requires specialized expertise (manual measurement and cardiologist interpretation), time (estimated at 20 minutes/scan), and equipment (ECG-gated cardiac CT scan and special software).
In this study, the researchers used a fully end-to-end automated system with analytic time measured in less than 2 seconds.
The team trained and tuned their CNN using the Framingham Heart Study Offspring and Third Generation cohorts (n = 1,636), which included asymptomatic patients with high-quality, cardiac-gated CT scans for CAC quantification.
The researchers then tested the CNN on two asymptomatic and two symptomatic cohorts:
- Asymptomatic Framingham Heart Study participants (n = 663) in whom the outcome measures were cardiovascular disease and death.
- Asymptomatic NLST participants (n = 14,959) in whom the outcome measure was atherosclerotic cardiovascular death.
- Symptomatic PROMISE study participants with stable chest pain (n = 4,021) in whom the outcome measures were all-cause mortality, MI, and hospitalization for unstable angina.
- Symptomatic ROMICAT-II study patients with acute chest pain (n = 441) in whom the outcome measure was acute coronary syndrome at 28 days.
Among 5,521 subjects across all testing cohorts with cardiac-gated and nongated chest CT scans, the CNN and expert reader interpretations agreed on the CAC risk scores with a high level of concordance (kappa, 0.71; concordance rate, 0.79).
There was a very high Spearman’s correlation of 0.92 (P < .0001) and substantial agreement between automatically and manually calculated CAC risk groups, substantiating robust risk prediction for cardiovascular disease across multiple clinical scenarios.
Dr. Aerts commented that, among the NLST participants who had the highest risk of developing lung cancer, the risk of cardiovascular death was as high as the risk of death from lung cancer.
Using AI to assess patient outcomes
In an unpublished study, Dr. Aerts and colleagues used AI in an attempt to determine whether changes in measurements of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle mass would provide clues about treatment outcomes in lung cancer patients.
The researchers developed a deep learning model using data from 1,129 patients at Massachusetts General and Brigham and Women’s Hospitals, measuring SAT, VAT, and muscle mass. The team applied the measurement system to a population of 12,128 outpatients and calculated z scores for SAT, VAT, and muscle mass to determine “normal” values.
When they applied the norms to surgical lung cancer data sets from the Boston Lung Cancer Study (n = 437) and TRACERx study (n = 394), the researchers found that smokers had lower adiposity and lower muscle mass than never-smokers.
More importantly, over time, among lung cancer patients who lost greater than 5% of VAT, SAT, and muscle mass, those patients with the greatest SAT loss (P < .0001) or VAT loss (P = .0015) had the lowest lung cancer–specific survival in the TRACERx study. There was no significant impairment of lung cancer-specific survival for patients who experienced skeletal muscle loss (P = .23).
The same observation was made for overall survival among patients enrolled in the Boston Lung Cancer Study, using the 5% threshold. Overall survival was significantly worse with increasing VAT loss (P = .0023) and SAT loss (P = .0082) but not with increasing skeletal muscle loss (P = .3).
The investigators speculated about whether the correlation between body composition and clinical outcome could yield clues about tumor biology. To test this, the researchers used the RNA sequencing–based ORACLE risk score in lung cancer patients from TRACERx. There was a high correlation between higher ORACLE risk scores and lower VAT and SAT, suggesting that measures of adiposity on CT were reflected in tumor biology patterns on an RNA level in lung cancer patients. There was no such correlation between ORACLE risk scores and skeletal muscle mass.
Wonderment ... tempered by concern and challenges
AI has awe-inspiring potential to yield actionable and prognostically important information from data mining the EHR and extracting the vast quantities of information from images. In some cases (like CAC), it is information that is “hiding in plain sight.” However, Dr. Aerts expressed several cautions, some of which have already plagued AI.
He referenced the Gartner Hype Cycle, which provides a graphic representation of five phases in the life cycle of emerging technologies. The “innovation trigger” is followed by a “peak of inflated expectations,” a “trough of disillusionment,” a “slope of enlightenment,” and a “plateau of productivity.”
Dr. Aerts noted that, in recent years, AI has seemed to fall into the trough of disillusionment, but it may be entering the slope of enlightenment on the way to the plateau of productivity.
His research highlighted several examples of productivity in radiomics in cancer patients and those who are at high risk of developing cancer.
In Dr. Aerts’s opinion, a second concern is replication of AI research results. He noted that, among 400 published studies, only 6% of authors shared the codes that would enable their findings to be corroborated. About 30% shared test data, and 54% shared “pseudocodes,” but transparency and reproducibility are problems for the acceptance and broad implementation of AI.
Dr. Aerts endorsed the Modelhub initiative (www.modelhub.ai), a multi-institutional initiative to advance reproducibility in the AI field and advance its full potential.
However, there are additional concerns about the implementation of radiomics and, more generally, data mining from clinicians’ EHRs to personalize care.
Firstly, it may be laborious and difficult to explain complex, computer-based risk stratification models to patients. Hereditary cancer testing is an example of a risk assessment test that requires complicated explanations that many clinicians relegate to genetics counselors – when patients elect to see them. When a model is not explainable, it undermines the confidence of patients and their care providers, according to an editorial related to the CXR-LC study.
Another issue is that uptake of lung cancer screening, in practice, has been underutilized by individuals who meet current, relatively straightforward Medicare criteria. Despite the apparently better accuracy of the CXR-LC deep-learning model, its complexity and limited access could constitute an additional barrier for the at-risk individuals who should avail themselves of screening.
Furthermore, although age and gender are accurate in most circumstances, there is legitimate concern about the accuracy of, for example, smoking history data and comorbid conditions in current EHRs. Who performs the laborious curation of the input in an AI model to assure its accuracy for individual patients?
Finally, it is unclear how scalable and applicable AI will be to medically underserved populations (e.g., smaller, community-based, free-standing, socioeconomically disadvantaged or rural health care institutions). There are substantial initial and maintenance costs that may limit AI’s availability to some academic institutions and large health maintenance organizations.
As the concerns and challenges are addressed, it will be interesting to see where and when the plateau of productivity for AI in cancer care occurs. When it does, many cancer patients will benefit from enhanced care along the continuum of the complex disease they and their caregivers seek to master.
Dr. Aerts disclosed relationships with Onc.AI outside the presented work.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
Artificial intelligence (AI) is expected to one day affect the entire continuum of cancer care – from screening and risk prediction to diagnosis, risk stratification, treatment selection, and follow-up, according to an expert in the field.
Hugo J.W.L. Aerts, PhD, director of the AI in Medicine Program at Brigham and Women’s Hospital in Boston, described studies using AI for some of these purposes during a presentation at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (Abstract IA-06).
In one study, Dr. Aerts and colleagues set out to determine whether a convolutional neural network (CNN) could extract prognostic information from chest radiographs. The researchers tested this theory using patients from two trials – the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and the National Lung Screening Trial (NLST).
The team developed a CNN, called CXR-risk, and tested whether it could predict the longevity and prognosis of patients in the PLCO (n = 52,320) and NLST (n = 5,493) trials over a 12-year time period, based only on chest radiographs. No clinical information, demographics, radiographic interpretations, duration of follow-up, or censoring were provided to the deep-learning system.
CXR-risk output was stratified into five categories of radiographic risk scores for probability of death, from 0 (very low likelihood of mortality) to 1 (very high likelihood of mortality).
The investigators found a graded association between radiographic risk score and mortality. The very-high-risk group had mortality rates of 53.0% (PLCO) and 33.9% (NLST). In both trials, this was significantly higher than for the very-low-risk group. The unadjusted hazard ratio was 18.3 in the PCLO data set and 15.2 in the NLST data set (P < .001 for both).
This association was maintained after adjustment for radiologists’ findings (e.g., a lung nodule) and risk factors such as age, gender, and comorbid illnesses like diabetes. The adjusted HR was 4.8 in the PCLO data set and 7.0 in the NLST data set (P < .001 for both).
In both data sets, individuals in the very-high-risk group were significantly more likely to die of lung cancer. The aHR was 11.1 in the PCLO data set and 8.4 in the NSLT data set (P < .001 for both).
This might be expected for people who were interested in being screened for lung cancer. However, patients in the very-high-risk group were also more likely to die of cardiovascular illness (aHR, 3.6 for PLCO and 47.8 for NSLT; P < .001 for both) and respiratory illness (aHR, 27.5 for PLCO and 31.9 for NLST; P ≤ .001 for both).
With this information, a clinician could initiate additional testing and/or utilize more aggressive surveillance measures. If an oncologist considered therapy for a patient with newly diagnosed cancer, treatment choices and stratification for adverse events would be more intelligently planned.
Using AI to predict the risk of lung cancer
In another study, Dr. Aerts and colleagues developed and validated a CNN called CXR-LC, which was based on CXR-risk. The goal of this study was to see if CXR-LC could predict long-term incident lung cancer using data available in the EHR, including chest radiographs, age, sex, and smoking status.
The CXR-LC model was developed using data from the PLCO trial (n = 41,856) and was validated in smokers from the PLCO trial (n = 5,615; 12-year follow-up) as well as heavy smokers from the NLST trial (n = 5,493; 6-year follow-up).
Results showed that CXR-LC was able to predict which patients were at highest risk for developing lung cancer.
CXR-LC had better discrimination for incident lung cancer than did Medicare eligibility in the PLCO data set (area under the curve, 0.755 vs. 0.634; P < .001). And the performance of CXR-LC was similar to that of the PLCOM2012 risk score in both the PLCO data set (AUC, 0.755 vs. 0.751) and the NLST data set (AUC, 0.659 vs. 0.650).
When they were compared in screening populations of equal size, CXR-LC was more sensitive than Medicare eligibility criteria in the PLCO data set (74.9% vs. 63.8%; P = .012) and missed 30.7% fewer incident lung cancer diagnoses.
AI as a substitute for specialized testing and consultation
In a third study, Dr. Aerts and colleagues used a CNN to predict cardiovascular risk by assessing coronary artery calcium (CAC) from clinically obtained, readily available CT scans.
Ordinarily, identifying CAC – an accurate predictor of cardiovascular events – requires specialized expertise (manual measurement and cardiologist interpretation), time (estimated at 20 minutes/scan), and equipment (ECG-gated cardiac CT scan and special software).
In this study, the researchers used a fully end-to-end automated system with analytic time measured in less than 2 seconds.
The team trained and tuned their CNN using the Framingham Heart Study Offspring and Third Generation cohorts (n = 1,636), which included asymptomatic patients with high-quality, cardiac-gated CT scans for CAC quantification.
The researchers then tested the CNN on two asymptomatic and two symptomatic cohorts:
- Asymptomatic Framingham Heart Study participants (n = 663) in whom the outcome measures were cardiovascular disease and death.
- Asymptomatic NLST participants (n = 14,959) in whom the outcome measure was atherosclerotic cardiovascular death.
- Symptomatic PROMISE study participants with stable chest pain (n = 4,021) in whom the outcome measures were all-cause mortality, MI, and hospitalization for unstable angina.
- Symptomatic ROMICAT-II study patients with acute chest pain (n = 441) in whom the outcome measure was acute coronary syndrome at 28 days.
Among 5,521 subjects across all testing cohorts with cardiac-gated and nongated chest CT scans, the CNN and expert reader interpretations agreed on the CAC risk scores with a high level of concordance (kappa, 0.71; concordance rate, 0.79).
There was a very high Spearman’s correlation of 0.92 (P < .0001) and substantial agreement between automatically and manually calculated CAC risk groups, substantiating robust risk prediction for cardiovascular disease across multiple clinical scenarios.
Dr. Aerts commented that, among the NLST participants who had the highest risk of developing lung cancer, the risk of cardiovascular death was as high as the risk of death from lung cancer.
Using AI to assess patient outcomes
In an unpublished study, Dr. Aerts and colleagues used AI in an attempt to determine whether changes in measurements of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle mass would provide clues about treatment outcomes in lung cancer patients.
The researchers developed a deep learning model using data from 1,129 patients at Massachusetts General and Brigham and Women’s Hospitals, measuring SAT, VAT, and muscle mass. The team applied the measurement system to a population of 12,128 outpatients and calculated z scores for SAT, VAT, and muscle mass to determine “normal” values.
When they applied the norms to surgical lung cancer data sets from the Boston Lung Cancer Study (n = 437) and TRACERx study (n = 394), the researchers found that smokers had lower adiposity and lower muscle mass than never-smokers.
More importantly, over time, among lung cancer patients who lost greater than 5% of VAT, SAT, and muscle mass, those patients with the greatest SAT loss (P < .0001) or VAT loss (P = .0015) had the lowest lung cancer–specific survival in the TRACERx study. There was no significant impairment of lung cancer-specific survival for patients who experienced skeletal muscle loss (P = .23).
The same observation was made for overall survival among patients enrolled in the Boston Lung Cancer Study, using the 5% threshold. Overall survival was significantly worse with increasing VAT loss (P = .0023) and SAT loss (P = .0082) but not with increasing skeletal muscle loss (P = .3).
The investigators speculated about whether the correlation between body composition and clinical outcome could yield clues about tumor biology. To test this, the researchers used the RNA sequencing–based ORACLE risk score in lung cancer patients from TRACERx. There was a high correlation between higher ORACLE risk scores and lower VAT and SAT, suggesting that measures of adiposity on CT were reflected in tumor biology patterns on an RNA level in lung cancer patients. There was no such correlation between ORACLE risk scores and skeletal muscle mass.
Wonderment ... tempered by concern and challenges
AI has awe-inspiring potential to yield actionable and prognostically important information from data mining the EHR and extracting the vast quantities of information from images. In some cases (like CAC), it is information that is “hiding in plain sight.” However, Dr. Aerts expressed several cautions, some of which have already plagued AI.
He referenced the Gartner Hype Cycle, which provides a graphic representation of five phases in the life cycle of emerging technologies. The “innovation trigger” is followed by a “peak of inflated expectations,” a “trough of disillusionment,” a “slope of enlightenment,” and a “plateau of productivity.”
Dr. Aerts noted that, in recent years, AI has seemed to fall into the trough of disillusionment, but it may be entering the slope of enlightenment on the way to the plateau of productivity.
His research highlighted several examples of productivity in radiomics in cancer patients and those who are at high risk of developing cancer.
In Dr. Aerts’s opinion, a second concern is replication of AI research results. He noted that, among 400 published studies, only 6% of authors shared the codes that would enable their findings to be corroborated. About 30% shared test data, and 54% shared “pseudocodes,” but transparency and reproducibility are problems for the acceptance and broad implementation of AI.
Dr. Aerts endorsed the Modelhub initiative (www.modelhub.ai), a multi-institutional initiative to advance reproducibility in the AI field and advance its full potential.
However, there are additional concerns about the implementation of radiomics and, more generally, data mining from clinicians’ EHRs to personalize care.
Firstly, it may be laborious and difficult to explain complex, computer-based risk stratification models to patients. Hereditary cancer testing is an example of a risk assessment test that requires complicated explanations that many clinicians relegate to genetics counselors – when patients elect to see them. When a model is not explainable, it undermines the confidence of patients and their care providers, according to an editorial related to the CXR-LC study.
Another issue is that uptake of lung cancer screening, in practice, has been underutilized by individuals who meet current, relatively straightforward Medicare criteria. Despite the apparently better accuracy of the CXR-LC deep-learning model, its complexity and limited access could constitute an additional barrier for the at-risk individuals who should avail themselves of screening.
Furthermore, although age and gender are accurate in most circumstances, there is legitimate concern about the accuracy of, for example, smoking history data and comorbid conditions in current EHRs. Who performs the laborious curation of the input in an AI model to assure its accuracy for individual patients?
Finally, it is unclear how scalable and applicable AI will be to medically underserved populations (e.g., smaller, community-based, free-standing, socioeconomically disadvantaged or rural health care institutions). There are substantial initial and maintenance costs that may limit AI’s availability to some academic institutions and large health maintenance organizations.
As the concerns and challenges are addressed, it will be interesting to see where and when the plateau of productivity for AI in cancer care occurs. When it does, many cancer patients will benefit from enhanced care along the continuum of the complex disease they and their caregivers seek to master.
Dr. Aerts disclosed relationships with Onc.AI outside the presented work.
Dr. Lyss was a community-based medical oncologist and clinical researcher for more than 35 years before his recent retirement. His clinical and research interests were focused on breast and lung cancers, as well as expanding clinical trial access to medically underserved populations. He is based in St. Louis. He has no conflicts of interest.
FROM AACR: AI, DIAGNOSIS, AND IMAGING 2021
FDA approves first drug that protects against chemo-induced myelosuppression
A novel drug that offers multilineage protection from chemotherapy-induced myelosuppression has been approved by the Food and Drug Administration.
The drug, trilaciclib (Cosela, G1 Therapeutics) is administered intravenously as a 30-minute infusion within 4 hours prior to the start of chemotherapy. It is indicated specifically for use in adults with extensive-stage small-cell lung cancer (ES-SCLC) who are receiving chemotherapy.
Trilaciclib is a CDK4/6 inhibitor, and this action appears to protect normal bone marrow cells from the harmful effects of chemotherapy.
“For patients with extensive-stage small-cell lung cancer, protecting bone marrow function may help make their chemotherapy safer and allow them to complete their course of treatment on time and according to plan,” Albert Deisseroth, MD, PhD, of the FDA’s Center for Drug Evaluation and Research, said in an FDA press release.
First drug of its type
Trilaciclib “is the first and only therapy designed to help protect bone marrow (myeloprotection) when administered prior to treatment with chemotherapy,” according to the drug’s manufacturer.
Myelosuppression is one of the most severe adverse effects of chemotherapy, and it can be life-threatening. It can increase the risk of infection and lead to severe anemia and/or bleeding.
“These complications impact patients’ quality of life and may also result in chemotherapy dose reductions and delays,” Jeffrey Crawford, MD, of Duke Cancer Institute, Durham, N.C., said in a company press release.
“To date, approaches have included the use of growth factor agents to accelerate blood cell recovery after the bone marrow injury has occurred, along with antibiotics and transfusions as needed. By contrast, trilaciclib provides the first proactive approach to myelosuppression through a unique mechanism of action that helps protect the bone marrow from damage by chemotherapy.”
Approval based on randomized, placebo-controlled trials
The approval of trilaciclib is based on data from three randomized, double-blind, placebo-controlled studies, involving a total of 245 patients with ES-SCLC.
These patients were being treated with chemotherapy regimens that were based on the combination of carboplatin and etoposide (with or without the immunotherapy atezolizumab) or regimens that were based on topotecan.
Before receiving the chemotherapy, patients were randomly assigned to receive trilaciclib or placebo.
Results showed that patients who had received an infusion of trilaciclib before receiving chemotherapy had a lower chance of developing severe neutropenia compared with patients who received a placebo, the FDA noted. In addition, among the patients who did develop severe neutropenia, this had a shorter duration among patients who received trilaciclib than among those who received placebo.
The most common side effects of trilaciclib were fatigue; low levels of calcium, potassium, and phosphate in the blood; increased levels of aspartate aminotransferase; headache; and pneumonia.
The FDA noted that patients should also be advised about injection site reactions, acute drug hypersensitivity, interstitial lung disease/pneumonitis, and embryo-fetal toxicity.
The approval received a priority review, based on the drug’s breakthrough therapy designation. As is common for such products, the company plans postmarketing activities that will assess the effects of trilaciclib on disease progression or survival with at least a 2-year follow up. This clinical trial is scheduled to start in 2022.
A version of this article first appeared on Medscape.com.
A novel drug that offers multilineage protection from chemotherapy-induced myelosuppression has been approved by the Food and Drug Administration.
The drug, trilaciclib (Cosela, G1 Therapeutics) is administered intravenously as a 30-minute infusion within 4 hours prior to the start of chemotherapy. It is indicated specifically for use in adults with extensive-stage small-cell lung cancer (ES-SCLC) who are receiving chemotherapy.
Trilaciclib is a CDK4/6 inhibitor, and this action appears to protect normal bone marrow cells from the harmful effects of chemotherapy.
“For patients with extensive-stage small-cell lung cancer, protecting bone marrow function may help make their chemotherapy safer and allow them to complete their course of treatment on time and according to plan,” Albert Deisseroth, MD, PhD, of the FDA’s Center for Drug Evaluation and Research, said in an FDA press release.
First drug of its type
Trilaciclib “is the first and only therapy designed to help protect bone marrow (myeloprotection) when administered prior to treatment with chemotherapy,” according to the drug’s manufacturer.
Myelosuppression is one of the most severe adverse effects of chemotherapy, and it can be life-threatening. It can increase the risk of infection and lead to severe anemia and/or bleeding.
“These complications impact patients’ quality of life and may also result in chemotherapy dose reductions and delays,” Jeffrey Crawford, MD, of Duke Cancer Institute, Durham, N.C., said in a company press release.
“To date, approaches have included the use of growth factor agents to accelerate blood cell recovery after the bone marrow injury has occurred, along with antibiotics and transfusions as needed. By contrast, trilaciclib provides the first proactive approach to myelosuppression through a unique mechanism of action that helps protect the bone marrow from damage by chemotherapy.”
Approval based on randomized, placebo-controlled trials
The approval of trilaciclib is based on data from three randomized, double-blind, placebo-controlled studies, involving a total of 245 patients with ES-SCLC.
These patients were being treated with chemotherapy regimens that were based on the combination of carboplatin and etoposide (with or without the immunotherapy atezolizumab) or regimens that were based on topotecan.
Before receiving the chemotherapy, patients were randomly assigned to receive trilaciclib or placebo.
Results showed that patients who had received an infusion of trilaciclib before receiving chemotherapy had a lower chance of developing severe neutropenia compared with patients who received a placebo, the FDA noted. In addition, among the patients who did develop severe neutropenia, this had a shorter duration among patients who received trilaciclib than among those who received placebo.
The most common side effects of trilaciclib were fatigue; low levels of calcium, potassium, and phosphate in the blood; increased levels of aspartate aminotransferase; headache; and pneumonia.
The FDA noted that patients should also be advised about injection site reactions, acute drug hypersensitivity, interstitial lung disease/pneumonitis, and embryo-fetal toxicity.
The approval received a priority review, based on the drug’s breakthrough therapy designation. As is common for such products, the company plans postmarketing activities that will assess the effects of trilaciclib on disease progression or survival with at least a 2-year follow up. This clinical trial is scheduled to start in 2022.
A version of this article first appeared on Medscape.com.
A novel drug that offers multilineage protection from chemotherapy-induced myelosuppression has been approved by the Food and Drug Administration.
The drug, trilaciclib (Cosela, G1 Therapeutics) is administered intravenously as a 30-minute infusion within 4 hours prior to the start of chemotherapy. It is indicated specifically for use in adults with extensive-stage small-cell lung cancer (ES-SCLC) who are receiving chemotherapy.
Trilaciclib is a CDK4/6 inhibitor, and this action appears to protect normal bone marrow cells from the harmful effects of chemotherapy.
“For patients with extensive-stage small-cell lung cancer, protecting bone marrow function may help make their chemotherapy safer and allow them to complete their course of treatment on time and according to plan,” Albert Deisseroth, MD, PhD, of the FDA’s Center for Drug Evaluation and Research, said in an FDA press release.
First drug of its type
Trilaciclib “is the first and only therapy designed to help protect bone marrow (myeloprotection) when administered prior to treatment with chemotherapy,” according to the drug’s manufacturer.
Myelosuppression is one of the most severe adverse effects of chemotherapy, and it can be life-threatening. It can increase the risk of infection and lead to severe anemia and/or bleeding.
“These complications impact patients’ quality of life and may also result in chemotherapy dose reductions and delays,” Jeffrey Crawford, MD, of Duke Cancer Institute, Durham, N.C., said in a company press release.
“To date, approaches have included the use of growth factor agents to accelerate blood cell recovery after the bone marrow injury has occurred, along with antibiotics and transfusions as needed. By contrast, trilaciclib provides the first proactive approach to myelosuppression through a unique mechanism of action that helps protect the bone marrow from damage by chemotherapy.”
Approval based on randomized, placebo-controlled trials
The approval of trilaciclib is based on data from three randomized, double-blind, placebo-controlled studies, involving a total of 245 patients with ES-SCLC.
These patients were being treated with chemotherapy regimens that were based on the combination of carboplatin and etoposide (with or without the immunotherapy atezolizumab) or regimens that were based on topotecan.
Before receiving the chemotherapy, patients were randomly assigned to receive trilaciclib or placebo.
Results showed that patients who had received an infusion of trilaciclib before receiving chemotherapy had a lower chance of developing severe neutropenia compared with patients who received a placebo, the FDA noted. In addition, among the patients who did develop severe neutropenia, this had a shorter duration among patients who received trilaciclib than among those who received placebo.
The most common side effects of trilaciclib were fatigue; low levels of calcium, potassium, and phosphate in the blood; increased levels of aspartate aminotransferase; headache; and pneumonia.
The FDA noted that patients should also be advised about injection site reactions, acute drug hypersensitivity, interstitial lung disease/pneumonitis, and embryo-fetal toxicity.
The approval received a priority review, based on the drug’s breakthrough therapy designation. As is common for such products, the company plans postmarketing activities that will assess the effects of trilaciclib on disease progression or survival with at least a 2-year follow up. This clinical trial is scheduled to start in 2022.
A version of this article first appeared on Medscape.com.