Linear screening and machine learning to identify suitable candidates for active breathing control after mastectomy in left-sided breast cancer.

PURPOSE: To evaluate the dosimetric benefit of deep inspiration breath-hold (DIBH) using the Active Breathing Coordinator (ABC) for left-sided breast cancer radiotherapy and to identify patients most likely to benefit based on free-breathing (FB) anatomical and clinical features. METHODS: Thirty-one postmastectomy patients underwent FB and ABC CT scans for volumetric modulated arc therapy planning. Thirteen anatomical features and clinical variables were extracted from FB images. Correlation analyses were performed to assess associations between FB-derived features and dose changes in organs at risk. Logistic regression (LR) and gradient boosting (GB) models were developed to predict two endpoints: a reduction in mean heart dose (MHD) >20% and a reduction in mean left lung dose (MLD) >10% with ABC compared with FB. SHapley Additive exPlanations (SHAP) analysis was used to interpret the GB models. RESULTS: ABC significantly reduced cardiac and pulmonary doses. Cardiopulmonary volume ratio (CVR) was strongly associated with mean MHD reduction, and patients with CVR >0.21 were more likely to achieve cardiac sparing with ABC. For predicting an MHD reduction >20%, the 5-feature GB model achieved an AUC of 0.93. For predicting an MLD reduction >10%, the 6-feature GB model achieved an AUC of 0.92. SHAP analysis confirmed the key role of CVR in predicting cardiac benefit and highlighted right lung volume as an indicator of inspiratory expansion potential. CONCLUSIONS: FB-based anatomical features can preliminarily identify patients likely to benefit from ABC. Interpretable ML models further improve patient selection and may support the use of ABC in breast cancer radiotherapy.