Construction and validation of a multimodal MRI-based deep learning model for early differential diagnosis of prostate cancer in the PSA gray zone: a retrospective cohort study

Background: The diagnostic challenges inherent in prostate-specific antigen (PSA) levels between 4-10 ng/mL represent a critical clinical dilemma, with only 25-30% of patients harboring clinically significant prostate cancer, leading to substantial rates of unnecessary biopsies and associated morbidity. Objective: To develop and validate a multimodal convolutional neural network integrating T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient maps, and clinical parameters for enhanced detection of clinically significant prostate cancer in the PSA gray zone. Methods: This retrospective cohort study analyzed 305 patients with PSA levels 4-10 ng/mL who underwent multiparametric MRI and subsequent biopsy confirmation. A novel multimodal CNN architecture based on modified U-Net with ResNet-50 backbone was developed, incorporating comprehensive fusion strategies. Decision curve analysis was performed to evaluate clinical utility across a range of threshold probabilities. Results: The proposed multimodal CNN achieved superior diagnostic performance with an area under the curve of 0.913 (95% CI: 0.851-0.975), sensitivity of 85.3% (71.4-94.2%), specificity of 90.9% (78.3-97.5%), and overall accuracy of 88.5% (78.2-95.1%), significantly outperforming PSA alone (AUC 0.592, p