A deep learning-based peer review method for radiotherapy planning

Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies.

This study aims to develop a novel peer review method for personalized QC in radiotherapy planning, which is based on patient anatomical information, and utilizes deep learning dose prediction and a statistical model.

A UNet model was trained on 139 nasopharyngeal carcinoma patients to predict 3D dose distribution, with plans divided into 95 for training, 20 for validation, and 24 for testing. For the clinical evaluation (24 items in total) of organs at risk (OAR), the QC interval (qualified, acceptable, or unqualified) for these items was set according to the model accuracy. Peer review was performed on another 29 clinical treatment plans, the items identified by the model as requiring optimization and improvement were optimized, and the effectiveness of the peer review method was tested.

The predicted mean voxel-based dose difference was 0.29 ± 0.13 Gy. For most evaluation items, the model prediction results were comparable to the planned results. Peer review results suggested that 66% of the plans were acceptable or unqualified. After optimization, 100% of the acceptable plans and 47% of the unqualified plans became qualified, and 20% of the unqualified plans became acceptable.

A deep learning dose prediction model based on patient information can be used to develop personalized QC in radiotherapy planning and can help improve the quality of radiotherapy plans.