Multimodal CNN outperforms PSA alone for prostate cancer diagnosis in biopsy-confirmed patients

BackgroundThe 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.ObjectiveTo 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.MethodsThis 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.ResultsThe 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