Edmund M Qiao, John He, Katrina D Silos, Jordan O Gasho, Patrick Belen, Danielle S Bitterman, Elizabeth McKenzie, Jennifer Steers, Christian Guthier, Anju Nohria, Michael T Lu, Hugo J W L Aerts, Andriana P Nikolova, Raymond H Mak, Katelyn M Atkins
{"title":"基线心房容量指数和胸部放疗后主要心脏不良事件。","authors":"Edmund M Qiao, John He, Katrina D Silos, Jordan O Gasho, Patrick Belen, Danielle S Bitterman, Elizabeth McKenzie, Jennifer Steers, Christian Guthier, Anju Nohria, Michael T Lu, Hugo J W L Aerts, Andriana P Nikolova, Raymond H Mak, Katelyn M Atkins","doi":"10.3389/fcvm.2025.1560922","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Patients 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.</p><p><strong>Methods: </strong>This multi-institutional retrospective study included 984 patients [<i>n</i> = 803 non-small cell lung cancer (NSCLC), <i>n</i> = 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.</p><p><strong>Results: </strong>In the discovery dataset (<i>n</i> = 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; <i>p</i> = 0.03]. In the validation cohorts (<i>n</i> = 102 NSCLC; <i>n</i> = 181 breast cancer), RAVI was associated with an increased risk of MACE (NSCLC: sHR 1.05, 95% CI: 1.001-1.106, <i>p</i> = 0.04; breast cancer: sHR 1.06, 95% CI: 1.01-1.11, <i>p</i> = 0.03). Similar findings were found for LAVI.</p><p><strong>Discussion: </strong>ML 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.</p>","PeriodicalId":12414,"journal":{"name":"Frontiers in Cardiovascular Medicine","volume":"12 ","pages":"1560922"},"PeriodicalIF":2.8000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170593/pdf/","citationCount":"0","resultStr":"{\"title\":\"Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy.\",\"authors\":\"Edmund M Qiao, John He, Katrina D Silos, Jordan O Gasho, Patrick Belen, Danielle S Bitterman, Elizabeth McKenzie, Jennifer Steers, Christian Guthier, Anju Nohria, Michael T Lu, Hugo J W L Aerts, Andriana P Nikolova, Raymond H Mak, Katelyn M Atkins\",\"doi\":\"10.3389/fcvm.2025.1560922\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Patients 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.</p><p><strong>Methods: </strong>This multi-institutional retrospective study included 984 patients [<i>n</i> = 803 non-small cell lung cancer (NSCLC), <i>n</i> = 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.</p><p><strong>Results: </strong>In the discovery dataset (<i>n</i> = 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; <i>p</i> = 0.03]. In the validation cohorts (<i>n</i> = 102 NSCLC; <i>n</i> = 181 breast cancer), RAVI was associated with an increased risk of MACE (NSCLC: sHR 1.05, 95% CI: 1.001-1.106, <i>p</i> = 0.04; breast cancer: sHR 1.06, 95% CI: 1.01-1.11, <i>p</i> = 0.03). Similar findings were found for LAVI.</p><p><strong>Discussion: </strong>ML 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. 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Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy.
Introduction: Patients 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.
Methods: This 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.
Results: In 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.
Discussion: ML 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.
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