Risk Estimation of Late Rectal Toxicity Using a Convolutional Neural Network-based Dose Prediction in Prostate Cancer Radiation Therapy

Purpose: The present study investigated the feasibility of our automatic plan generation model based on a convolutional neural network (CNN) to estimate the baseline risk of grade >= 2 late rectal bleeding (G2-LRB) in volumetric modulated arc therapy for prostate cancer. Methods and Materials: We built the 2-dimensional U-net model to predict dose distributions using the planning computed tomography and organs at risk masks as inputs. Seventy-five volumetric modulated arc therapy plans of prostate cancer, which were delivered at 74.8 Gy in 34 fractions with a uniform planning goal, were included: 60 for training and 5-fold cross-validation, and the remaining 15 for testing. Isodose volume dice similarity coefficient, dose-volume histogram, and normal tissue complication probability (NTCP) metrics between planned and CNN-predicted dose distributions were calculated. The primary endpoint was the goodness-of-fit, expressed as a coefficient of determination (R-2) value, in predicting the percentage of G2-LRB-Lyman-Kutcher-Burman-NTCP. Results: In 15 test cases, 2-dimensional U-net predicted dose distributions with a mean isodose volume dice similarity coefficient value of 0.90 within the high-dose region (doses >= 50 Gy). Rectum V-50Gy, V-60Gy, and V-70Gy were accurately predicted (R-2 = 0.73, 0.82, and 0.87, respectively). Strong correlations were observed between planned and predicted G2-LRB-Lyman-Kutcher-Burman-NTCP (R-2 = 0.80, P < .001), with a small percent mean absolute error (mean +/- 1 standard deviation, 1.24% +/- 1.42%). Conclusions: A risk estimation of LRB using CNN-based automatic plan generation from anatomic information was feasible. These results will contribute to the development of a decision support system that identifies priority cases for preradiation therapy interventions, such as hydrogel spacer implantation. (c) 2025 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).