ArticleQuantitative imaging in medicine and surgery2025
Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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: Compared with the state-of-the-art DL architectures [pix2pix, DoseDiff (distance-aware diffusion model), and MD-Dose (diffusion model based on the Mamba)], the 3D MT-U-Net model demonstrated substantially improved prediction accuracy respectively, with notable reductions in MAE values for planning target volume (PTV)1 (0.016±0.023 Conclusions: This study developed a fully automated IMRT plan design method for rectal cancer. This approach significantly improved the efficiency of designing IMRT plans and plan quality.
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