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<0.001) and PI-RADS assessment (AUC 0.694, p<0.001). Nested 5-fold cross-validation demonstrated consistent performance across folds (AUC range: 0.891-0.928), while extended bootstrap validation with 5,000 iterations confirmed robust stability (AUC standard deviation: 0.032). Inter-reader agreement between the model and expert radiologists demonstrated excellent concordance (κ=0.871, 95% CI: 0.831-0.911). Decision curve analysis confirmed a consistently superior net benefit for the multimodal CNN across clinically relevant threshold probabilities.

CONCLUSIONS

The multimodal deep learning approach represents a paradigm shift in non-invasive prostate cancer detection, potentially reducing unnecessary biopsies by 40-50% while maintaining exceptional sensitivity for clinically significant disease. Decision curve analysis substantiates the clinical utility of this approach across a broad range of decision thresholds.