INTRODUCTION

Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two-stage model designed for precise Gleason grading. The model initially uses radiomics-based multiparametric MRI to identify csPCa and then refines the Gleason grading by integrating clinical indicators and radiomics features.

METHODS

We retrospectively analysed 399 patients with PI-RADS ≥ 3 lesions, categorising them into non-significant prostate cancer (nsPCa, 263 cases) and csPCa (136 cases, subdivided by GGs). Regions of interest (ROIs) for the prostate and lesions were manually delineated on T2-weighted and apparent diffusion coefficient (ADC) images, followed by the extraction of radiomics features. A two-stage model was developed: the first stage identifies csPCa using radiomics-based MRI, and the second integrates clinical indicators for Gleason grading. Model efficacy was evaluated by sensitivity, specificity, accuracy and area under the curve (AUC), with external validation on 100 patients.

RESULTS

The first-stage model demonstrated excellent diagnostic accuracy for csPCa, achieving AUCs of 0.989, 0.982 and 0.976 in the training, testing and external validation cohorts, respectively. The second-stage model exhibited commendable Gleason grading capabilities, with AUCs of 0.82, 0.844 and 0.83 across the same cohorts. Decision curve analysis supported the clinical applicability of both models.

CONCLUSIONS

This study validated the potential of T2W and ADC image radiomics features as biomarkers in distinguishing csPCa. Combining these features with clinical indicators for csPCa Gleason grading provides superior predictive performance and significant clinical benefit.