A Multimodal Deep Learning Nomogram for the Identification of Clinically Significant Prostate Cancer in Patients with Gray-Zone PSA Levels: Comparison with Clinical and Radiomics Models.

Rationale And Objectives: To establish a multimodal deep learning nomogram for predicting clinically significant prostate cancer in patients with gray-zone PSA levels.

Methods: This retrospective study enrolled 303 patients with pathological results between January 2018 and December 2022. Clinical variables and the PI-RADS v2.1 score were used to construct a clinical model. Radiomics and deep learning features from bp-MRI were used to develop a radiomics model with SVM and a deep learning model, respectively. A hybrid fusion approach was used to integrate the multimodal data and construct combined models (Comb.Rad.model and Comb.DL.model). The robustness of the radiomics model with XGBoost was validated and compared. Model efficacy was assessed through ROC curve and decision curve analysis. A nomogram was developed based on the best-performing model.

Results: The clinical model had AUCs of 0.845 and 0.779 in the training and testing set. The radiomics model with SVM and the deep learning model achieved AUCs of 0.825 and 0.933 in the training set and 0.811 and 0.907 in the testing set, respectively. The diagnostic performance of the combined models was significantly improved, with Comb.DL.model having a higher AUC than Comb.Rad.model in both the training (0.986 vs. 0.924, P = 0.008) and testing (0.965 vs. 0.859, P = 0.005) set. The diagnostic efficiency of both the radiomics model and Comb.Rad.model with XGBoost were comparable to that of SVM, confirming the robustness of the established model.

Conclusion: The integrated nomogram combining deep learning features, PI-RADS score, and clinical variables significantly outperformed the traditional radiomics and clinical models.