To precisely predict PCa risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features.

Between August 2016 and May 2021, patients with histologically proven PCa who underwent preoperative MRI and prostate-specific antigen screening were included. The patients were grouped into different risk categories as defined by the EAU-EANM-ESTRO-ESUR-SIOG guidelines. Using Artificial Intelligence Kit software, PCa regions of interest were delineated and radiomic features were extracted. Subsequently, predictable models were built by utilizing five traditional ML approaches: support vector machine (SVM), logistic regression (LR), gradient boosting decision tree (GBDT), k-nearest neighbour (KNN) and random forest (RF) classifiers. The classification capacity of the developed models was assessed by area under the receiver operating characteristic curve (AUC) analysis.

A total of 213 patients were enrolled, including 16 low-risk, 65 intermediate-risk, and 132 high-risk PCa patients. The risk stratification of PCa could be revealed by MRI radiomic features, and second-order features accounted for most of the selected features. Among the five established ML models, the RF model showed the best overall predictive performance (AUC = 0.87). After further analysis of the subgroups based on the RF model, the prediction of the high-risk group was the best (AUC = 0.89).

This study demonstrated that the MR radiomics-based ML method could be a promising tool for predicting PCa risk stratification precisely.

The ML models have valuable prospect for accurate PCa risk assessment, which might contribute to customize treatment and surveillance strategies.