Interpretable multimodal deep learning model for predicting post-surgical international society of urological pathology grade in primary prostate cancer.

PURPOSE: To address heterogeneity in prostate cancer (PCa) pathological grading, we developed an interpretable multimodal fusion model integrating 18F prostate-specific membrane antigen (18F-PSMA)-targeted positron emission tomography/computed tomography (18F-PSMA-PET/CT) imaging features with clinical variables for predicting post-surgical ISUP grade (psISUP ≥ 4 vs. < 4).

METHODS

This retrospective study analyzed 222 patients with PCa (2020-2024) undergoing 18F-PSMA PET/CT. We constructed a deep transfer learning framework incorporating radiomic features from PET/CT and clinical parameters. Model performance was validated against three established methods and preoperative biopsy Gleason scores. Additionally, SHapley Additive exPlanations (SHAP) values elucidated feature contributions, and a radiomic nomogram was developed for clinical translation.

RESULTS

The fusion model achieved superior discrimination in psISUP grading (test set area under the curve (AUC) = 0.850, 95% confidence interval [CI] 0.769-0.932; validation set AUC = 0.833, 95% CI 0.657-1.000), significantly outperforming preoperative Gleason scores. SHAP analysis identified PSMA uptake heterogeneity and PSA density as key predictive features. The nomogram demonstrated clinical interpretability through visualised risk stratification.

CONCLUSION

Our deep learning-based multimodal fusion model enables accurate preoperative prediction of aggressive PCa pathology (ISUP ≥ 4), potentially optimising surgical planning and personalised therapeutic strategies. The interpretable framework enhances clinical trustworthiness in artificial intelligence-assisted decision-making.