Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer

Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates clinical use such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners allowed manually adapt them. Plans evaluated based on goals and DVH parameters. Thirty-seven 50plans did fulfill all without adjustments. Thirteen these 37 still adjusted but not improve mean heart or lung dose. These results leave room improvement both DL as well education clinically relevant