ArticleJournal of applied clinical medical physics2026
Machine learning predictors of post-radiotherapy cardiotoxicity in a Latin American breast cancer cohort: Clinical risk factors outperform cardiac dosimetry.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMost evidence on post-radiotherapy cardiotoxicity (CDT) derives from Global North cohorts, and the relative contribution of baseline clinical risk versus cardiac dosimetry remains poorly characterized in Latin American populations. PURPOSE: To examine, in a Chilean breast cancer cohort, whether baseline clinical risk factors or cardiac dose-volume metrics better predict post-RT cardiac events, using supervised and unsupervised learning.
methodsRetrospective cohort of 42 breast cancer patients treated with thoracic radiotherapy (RT, median heart dose 2.6 Gy; median follow-up 84.4 months). CDT was defined per CTCAE v5.0 (
resultsTen patients (23.8%) developed CDT and 16 (38.1%) experienced cardiovascular events. No DVH differences were found between CDT+ and CDT- groups across any cardiac structure (all
conclusionsIn this low-cardiac-dose cohort, baseline clinical variables outperformed cardiac dose-volume metrics in predicting post-RT cardiac events, although the design was not powered to exclude a dose-effect relationship at these exposures. Registry-based baseline risk assessment is therefore the more robust starting point in this setting, and IMRT breast RT planning warrants multi-substructure cardiac dosimetry monitoring.
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