Predictive Model of Acute Rectal Toxicity in Prostate Cancer Treated With Radiotherapy.

Abstract

To aid personalized treatment selection, we developed a predictive model for acute rectal toxicity in patients with prostate cancer undergoing radiotherapy with photons and protons. We analyzed a prospective multi-institutional cohort of 278 patients treated from 2012 to 2023 across 10 centers. Dosimetric and nondosimetric variables were collected, and key predictors were identified using purposeful feature selection. The cohort was split into discovery (n = 227) and validation (n = 51) data sets. The dose along the rectum surface was transformed into a two-dimensional surface, and dose-area histograms (DAHs) were quantified. A convolutional neural network (CNN) was developed to extract dosimetric features from the DAH and integrate them with nondosimetric predictors. Model performance was benchmarked against logistic regression (LR) using the AUC. Key predictors included rectum length, race, age, and hydrogel spacer use. The CNN model demonstrated stability in the discovery data set (AUC = 0.81 ± 0.11) and outperformed LR in the validation data set (AUC = 0.81 <i>v</i> 0.54). Separate analysis of photon and proton subsets yielded consistent AUCs of 0.7 and 0.92, respectively. In the photon high-risk group, the model achieved 83% sensitivity, and in proton subsets, it achieved 100% sensitivity and specificity, indicating the potential to be used for treatment selection in these patients. Our novel approach effectively predicts rectal toxicity across photon and proton subsets, demonstrating the utility of integrating dosimetric and nondosimetric features. The model's strong performance across modalities suggests potential for guiding treatment decisions, warranting prospective validation.