Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy
IntroductionPatients receiving thoracic radiotherapy (RT) have an increased risk of major adverse cardiac events (MACE) posttreatment. We utilized machine learning (ML) to discover novel predictors of MACE and validated them on an external cohort.MethodsThis multi-institutional retrospective study i...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| author | Edmund M. Qiao John He Katrina D. Silos Jordan O. Gasho Patrick Belen Danielle S. Bitterman Danielle S. Bitterman Elizabeth McKenzie Jennifer Steers Christian Guthier Anju Nohria Michael T. Lu Hugo J. W. L. Aerts Hugo J. W. L. Aerts Hugo J. W. L. Aerts Andriana P. Nikolova Raymond H. Mak Raymond H. Mak Katelyn M. Atkins Katelyn M. Atkins |
| author_facet | Edmund M. Qiao John He Katrina D. Silos Jordan O. Gasho Patrick Belen Danielle S. Bitterman Danielle S. Bitterman Elizabeth McKenzie Jennifer Steers Christian Guthier Anju Nohria Michael T. Lu Hugo J. W. L. Aerts Hugo J. W. L. Aerts Hugo J. W. L. Aerts Andriana P. Nikolova Raymond H. Mak Raymond H. Mak Katelyn M. Atkins Katelyn M. Atkins |
| author_sort | Edmund M. Qiao |
| collection | DOAJ |
| description | IntroductionPatients receiving thoracic radiotherapy (RT) have an increased risk of major adverse cardiac events (MACE) posttreatment. We utilized machine learning (ML) to discover novel predictors of MACE and validated them on an external cohort.MethodsThis multi-institutional retrospective study included 984 patients [n = 803 non-small cell lung cancer (NSCLC), n = 181 breast cancer] treated with radiotherapy. Extreme gradient boosting was utilized to discover novel clinical, dosimetric, and anatomical features (CT-based cardiac substructure segmentations) associated with MACE in a cohort of locally advanced NSCLC patients. Fine–Gray regression was performed with non-cardiac death as a competing risk. External validation was performed utilizing independent cohorts of NSCLC or breast cancer patients.ResultsIn the discovery dataset (n = 701), 70 patients experienced MACE. ML modeling (training AUC, 0.68; testing AUC, 0.71) identified right and left atrial volume indices (RAVI and LAVI, respectively) as top predictors. After adjusting for baseline cardiovascular risk and known radiotherapy predictive factors, RAVI was associated with an increased risk of MACE [subdistribution hazard ratio (sHR) 1.02/unit, 95% confidence interval (CI): 1.00–1.04; p = 0.03]. In the validation cohorts (n = 102 NSCLC; n = 181 breast cancer), RAVI was associated with an increased risk of MACE (NSCLC: sHR 1.05, 95% CI: 1.001–1.106, p = 0.04; breast cancer: sHR 1.06, 95% CI: 1.01–1.11, p = 0.03). Similar findings were found for LAVI.DiscussionML modeling identified right and left atrial enlargement as novel radiographic predictors for increased risk of MACE following chest radiotherapy, which was validated in independent breast and lung cancer datasets. Given that echocardiography studies have demonstrated the prognostic utility of atrial volume indices across cardiovascular risk groups, these findings warrant further study to identify additional strategies for upfront cardiovascular risk profiling. |
| format | Article |
| id | doaj-art-d9b885e2f69140bf8d0edddd9cf740e1 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-d9b885e2f69140bf8d0edddd9cf740e12025-08-20T03:25:59ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-06-011210.3389/fcvm.2025.15609221560922Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapyEdmund M. Qiao0John He1Katrina D. Silos2Jordan O. Gasho3Patrick Belen4Danielle S. Bitterman5Danielle S. Bitterman6Elizabeth McKenzie7Jennifer Steers8Christian Guthier9Anju Nohria10Michael T. Lu11Hugo J. W. L. Aerts12Hugo J. W. L. Aerts13Hugo J. W. L. Aerts14Andriana P. Nikolova15Raymond H. Mak16Raymond H. Mak17Katelyn M. Atkins18Katelyn M. Atkins19Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, United StatesDepartment of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, United StatesDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, United StatesArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham and Harvard Medical School, Boston, MA, United StatesDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, United StatesDepartment of Cardiovascular Medicine, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, United StatesDepartment of Radiology, Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United StatesDepartment of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, United StatesArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham and Harvard Medical School, Boston, MA, United StatesRadiology and Nuclear Medicine, GROW & CARIM Maastricht University, Maastricht, NetherlandsDepartment of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, United StatesArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham and Harvard Medical School, Boston, MA, United StatesDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesDepartment of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United StatesIntroductionPatients receiving thoracic radiotherapy (RT) have an increased risk of major adverse cardiac events (MACE) posttreatment. We utilized machine learning (ML) to discover novel predictors of MACE and validated them on an external cohort.MethodsThis multi-institutional retrospective study included 984 patients [n = 803 non-small cell lung cancer (NSCLC), n = 181 breast cancer] treated with radiotherapy. Extreme gradient boosting was utilized to discover novel clinical, dosimetric, and anatomical features (CT-based cardiac substructure segmentations) associated with MACE in a cohort of locally advanced NSCLC patients. Fine–Gray regression was performed with non-cardiac death as a competing risk. External validation was performed utilizing independent cohorts of NSCLC or breast cancer patients.ResultsIn the discovery dataset (n = 701), 70 patients experienced MACE. ML modeling (training AUC, 0.68; testing AUC, 0.71) identified right and left atrial volume indices (RAVI and LAVI, respectively) as top predictors. After adjusting for baseline cardiovascular risk and known radiotherapy predictive factors, RAVI was associated with an increased risk of MACE [subdistribution hazard ratio (sHR) 1.02/unit, 95% confidence interval (CI): 1.00–1.04; p = 0.03]. In the validation cohorts (n = 102 NSCLC; n = 181 breast cancer), RAVI was associated with an increased risk of MACE (NSCLC: sHR 1.05, 95% CI: 1.001–1.106, p = 0.04; breast cancer: sHR 1.06, 95% CI: 1.01–1.11, p = 0.03). Similar findings were found for LAVI.DiscussionML modeling identified right and left atrial enlargement as novel radiographic predictors for increased risk of MACE following chest radiotherapy, which was validated in independent breast and lung cancer datasets. Given that echocardiography studies have demonstrated the prognostic utility of atrial volume indices across cardiovascular risk groups, these findings warrant further study to identify additional strategies for upfront cardiovascular risk profiling.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1560922/fulloncologyradiotherapylungbreastmajor adverse cardiac eventsatrial volume |
| spellingShingle | Edmund M. Qiao John He Katrina D. Silos Jordan O. Gasho Patrick Belen Danielle S. Bitterman Danielle S. Bitterman Elizabeth McKenzie Jennifer Steers Christian Guthier Anju Nohria Michael T. Lu Hugo J. W. L. Aerts Hugo J. W. L. Aerts Hugo J. W. L. Aerts Andriana P. Nikolova Raymond H. Mak Raymond H. Mak Katelyn M. Atkins Katelyn M. Atkins Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy Frontiers in Cardiovascular Medicine oncology radiotherapy lung breast major adverse cardiac events atrial volume |
| title | Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy |
| title_full | Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy |
| title_fullStr | Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy |
| title_full_unstemmed | Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy |
| title_short | Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy |
| title_sort | baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy |
| topic | oncology radiotherapy lung breast major adverse cardiac events atrial volume |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1560922/full |
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