Optimizing Cardiac Dose Prediction in Left-Sided Breast Cancer Radiation Therapy: Clinical Strategies for Identifying Beneficiaries of the Deep Inspiration Breath-Hold Technique.

Purpose: Deep inspiration breath-hold (DIBH) is widely used in breast cancer radiation therapy to reduce cardiac radiation exposure. However, not all patients benefit from it. This study evaluated the performance of a convolutional neural network (CNN) model to predict cardiac doses under free-breathing (FB) and DIBH conditions for left-sided breast cancer radiation therapy, aiming to identify patients most likely to benefit from DIBH based on cardiac dosimetry. Methods and Materials: A total of 265 left-sided breast cancer patients undergoing whole-breast irradiation were included, with 200 retrospectively assigned to the training set and 65 prospectively assigned to the test set. The CNN model incorporated anatomic data, including organ structures and distance-to-target volume maps, to predict 3-dimensional dose distributions. Predicted dosimetric parameters were compared with clinical data to assess accuracy, and agreement between model-based and clinical classifications of DIBH benefit was evaluated using kappa statistics. Results: The CNN model demonstrated high accuracy in predicting cardiac dosimetric parameters, with correlation coefficients ranging from 0.84 to 0.99 for mean dose (D Conclusions: The CNN-based model provides an efficient and accurate framework for predicting cardiac dose and identifying patients most likely to benefit from DIBH. Future studies should explore its applicability across broader radiation therapy scenarios and evaluate its long-term impact on cardiac outcomes.