A deep learning-based dose prediction method for evaluation of radiotherapy treatment planning

To address the issue of low accuracy in dose distribution prediction in radiotherapy, we propose a deep learning-based model for predicting three-dimensional dose distribution in tumor radiation therapy. The model utilizes quantitative evaluation methods to assess the treatment plans. We selected a dataset of 130 cervical cancer patients, including CT images and target region files. A deep learning U-Net model based on convolutional neural networks and residual blocks was employed to automatically extract multi-scale and multi-level feature maps of CT images, target regions, and anatomical structures of critical organs for intensity-modulated radiation therapy (IMRT) treatment plans and perform three-dimensional dose distribution prediction. Quantitative analysis methods, including error measures such as maximum dose (Dmax), mean dose (Dmean), V20, and D95, were used. For cervical cancer cases, DVH (dose-volume histogram) graphs were generated based on the evaluation results, directly reflecting the differences between the actual and predicted doses. The actual errors met the basic requirements, and a quantitative evaluation approach was used to optimize the dosimetric parameters. The specific quantification results are: DSC: 86. 52 ± 9. 31, 95% HD: 3. 74 ± 1. 49, JD: 0. 112 ± 0. 026, MSD: 0. 067 ± 0. 031. Through training the deep learning model, we have successfully captured the complex nonlinear relationship between IMRT plan feature map parameters and three-dimensional dose distribution. In practical clinical applications, this trained model can accurately predict personalized three-dimensional dose distribution for new patients and effectively assess treatment plans in a quantitative manner. The source code is available at: https: //github. com/xiebw9509/Radiotherapydoseₚrediction.