Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.

purposeThis study aimed to develop a deep learning model based on magnetic resonance imaging (MRI) and clinical features for predicting PSM risk after radical prostatectomy (RP).

methodsThis retrospective multicenter study included 1177 prostate cancer patients who underwent preoperative MRI and RP across eight institutions. A total of 1022 patients from two institutions were used for model training, while 155 patients from six independent centers formed the external validation cohort. A feature disentanglement-based deep learning model (DESM) was developed to isolate disease-specific features from hospital-specific variations. A multimodal fusion model (MDESM) was further constructed by integrating the DESM-derived imaging signature with clinical variables to enhance prediction accuracy and generalizability. Gradient-weighted class activation mapping was applied to provide interpretability by highlighting model attention regions.

resultsIn the external validation cohort, MDESM achieved an area under the receiver operating characteristic curve of 0.843 (95% CI, 0.770-0.911), significantly outperforming the DESM (0.711, 95% CI, 0.607-0.800, p = 0.003, Z = 2.936) and the clinical-only model (0.676, 95% CI, 0.577-0.770, p < 0.001, Z = 3.420). Decision curve analysis demonstrated the highest net benefit for MDESM across a range of threshold probabilities.

conclusionThe MDESM model, based on a feature disentanglement and multimodal fusion strategy, demonstrates the potential to effectively combine MRI and clinical data to achieve accurate PSM prediction. This approach offers a promising tool for preoperative risk stratification and surgical planning in prostate cancer.