Clinical and MRI features for predicting maximum cancer involvement of >50% in a single biopsy core among patients diagnosed with prostate cancer at initial systematic biopsy: a machine learning study.

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Abstract

To investigate the conditional predictive value of prebiopsy clinical variables and magnetic resonance imaging (MRI) features for maximum cancer involvement of >50% in a single biopsy core among patients diagnosed with prostate cancer at initial systematic biopsy and to compare the internally validated performance of different machine learning models. This retrospective study included patients diagnosed with prostate cancer at initial systematic biopsy at the First Affiliated Hospital of Dali University between April 2023 and February 2026. Clinical, imaging, and combined models were developed to predict maximum cancer involvement of >50% in a single biopsy core using age, body mass index, total prostate-specific antigen (tPSA), prostate volume, Prostate Imaging Reporting and Data System (PI-RADS) score, and MRI features related to tumor extent. Logistic regression, elastic net, random forest, and extreme gradient boosting (XGBoost) were compared using the combined predictors. Discrimination, calibration, and overall prediction error were evaluated using five repeats of stratified five-fold cross-validation and bootstrap resampling. Among 127 patients, 85 (66.9%) had maximum cancer involvement of >50% in a single biopsy core. The areas under the receiver operating characteristic curve (AUROCs) of the clinical, imaging, and combined models were 0.822, 0.773, and 0.823, respectively. The combined model showed no clear incremental discrimination over the clinical model but had a lower overall prediction error than the imaging model. Among the four algorithms using the combined predictors, elastic net achieved the highest AUROC (0.823), whereas XGBoost achieved the highest average precision (0.878), the lowest Brier score (0.151), and favorable calibration. tPSA and PI-RADS score showed relatively consistent predictive contributions across models. Sensitivity analyses yielded generally similar AUROCs, although Brier scores and calibration slopes varied. Prebiopsy clinical variables and MRI features enabled risk stratification for maximum cancer involvement of >50% in a single biopsy core among patients diagnosed with prostate cancer at initial systematic biopsy. Elastic net and XGBoost demonstrated favorable overall predictive performance, while tPSA and PI-RADS score showed relatively consistent contributions. External validation in independent cohorts is required.

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