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 V50Gy, V60Gy, and V70Gy 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.