ADC-based radiomics for risk stratification in prostate cancer: a clinical decision support study

Reliable differentiation between malignant and benign prostate lesions remains a critical challenge in clinical practice, particularly in settings with limited access to expert interpretation of multiparametric MRI (mpMRI). We investigated whether a streamlined radiomics framework based solely on apparent diffusion coefficient (ADC) imaging could provide accurate, reproducible, and interpretable diagnostic support for prostate cancer detection.

We retrospectively analyzed ADC images from 198 men, including 121 patients with histologically confirmed prostate cancer and 77 patients without malignancy, acquired between 2020 and 2025. Following image resampling and standardization, 107 radiomic features were extracted and subjected to hierarchical feature selection using recursive feature elimination followed by LASSO regression. Four discriminative features were retained to train support vector machine, random forest, logistic regression, and gradient boosting decision tree (GBDT) classifiers. Model performance was evaluated in independent training and testing cohorts using accuracy, AUC, precision, recall, F1 score, and specificity.

The GBDT model demonstrated the most robust performance, achieving AUC of 0.81 (95% CI: 0.75-0.87) in the training cohort and 0.78 (95% CI: 0.72-0.85) in the testing cohort. In the testing cohort, the model achieved an accuracy of 0.76 (95% CI: 0.69-0.84), a recall of 1.00 (95% CI: 1.00-1.00), an F1 score of 0.86 (95% CI: 0.80-0.92), and a specificity of 0.73 (95% CI: 0.62-0.88). The selected radiomic features reflected image energy, textural irregularity, and intralesional heterogeneity and were consistently associated with malignant pathology.

A minimal-feature radiomics model derived exclusively from ADC images showed promising diagnostic performance for differentiating malignant from benign prostate lesions. By avoiding reliance on multiparametric imaging, this streamlined approach may provide a practical, reproducible, and resource-efficient strategy for supporting prostate cancer diagnosis, particularly in settings where expert mpMRI interpretation is limited. Further multicenter validation, integration of clinical variables, and incorporation of explainable artificial intelligence approaches are warranted to establish its robustness and enhance its translational potential.