Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions

作者：Yimei Liu, Lin Huang, Zixuan Leng, Meining Chen, Jun Zhang, R.C. Chen, Zhaocai Chen, Yaoying Liu, Zhenyu Qi, Qichao Zhou, Xiaowu Deng, Yinglin Peng · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2025 · DOI：10.21037/qims-2025-168 · 被引用次数：2 · 研究领域：Advanced Radiotherapy Techniques、Effects of Radiation Exposure、Radiomics and Machine Learning in Medical Imaging

Background: Designing intensity-modulated radiotherapy (IMRT) plans for rectal cancer is complex and time-consuming. We used a three-dimensional (3D) multitask training U-Net (3D MT-U-Net) deep learning (DL) model to accurately predict radiotherapy dose distributions for rectal cancer. We aimed to achieve fully automated IMRT plans with improved efficiency and quality. Methods: We developed a 3D MT-U-Net model that precisely captured dose distribution characteristics through pretraining and a multitask learning mechanism. In the multitask learning module, we additionally introduced the learning of gradient maps and isodose line maps to enhance the network's ability to extract semantic information from dose distributions. The patients were divided into a training set (n=99), an independent test set (n=26), and an external test set (n=15). The high-precision dose predictions were translated into automated optimization objectives by integrating clinical constraints to establish two fully automated optimization methods based on 3D voxel dose and dose-volume histogram (DVH) parameters. The Monte Carlo algorithm was used to perform dose calculations and achieve fully automated plans. Using manually designed plans as a reference, the dose distributions predicted by the model and generated by the automated plans were evaluated using the mean absolute error (MAE) and DVH parameters. Results: 0.033±0.019, 0.038±0.008, and 0.029±0.005). Both automated planning methods [voxel dose-based ...