Radiation exposure and clinical validation of autosegmentation models for the supraventricular cardiac conduction system in breast cancer radiotherapy: an institutional perspective.

Background: Radiation dose to cardiac conduction nodes may contribute to arrhythmia risks in breast cancer (BC) patients after radiotherapy, yet dosimetric evidence remains limited. This study aimed to evaluate doses to the sinoatrial (SAN) and atrioventricular nodes (AVN) in BC patients treated with intensity-modulated radiation therapy (IMRT) and to clinically validate a deep learning-based autosegmentation model for these structures. Methods: A retrospective analysis was conducted on 87 BC patients who underwent IMRT. Doses to the whole heart, four cardiac chambers, the SAN, and the AVN were evaluated and correlated. For autosegmentation, a convolutional neural network (CNN) was trained on 60 patients, validated on seven, and tested on 20. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC), and dosimetric consistency was compared between automated and manual contours. Results: In right-sided BC patients, the SAN received the highest mean dose among cardiac substructures (5.43 Gray [Gy]) under a mean heart dose of 3.39 Gy. Both SAN and AVN doses showed strong correlations with right atrial (RA) dose ( Conclusions: The SAN receives substantial irradiation in right-sided BC patients during IMRT, and RA dose strongly correlates with conduction node doses, suggesting its potential as a clinical surrogate. The CNN-based autosegmentation method enables accurate and efficient delineation of the SAN and AVN, facilitating reliable dosimetric assessment in clinical practice.