Predicting Response to Neoadjuvant Hormonal Therapy and Prognostic Outcomes in High-Risk Prostate Cancer Using MRI-Based Radiomics

Purpose This study aimed to develop and validate an MRI-based radiomics model for predicting complete pathological response (pCR) to neoadjuvant hormonal therapy (NHT) and to assess its prognostic value in patients with high-risk prostate cancer (HRPCa). Methods This retrospective study included 140 patients with HRPCa who underwent NHT followed by radical prostatectomy at Huadong Hospital. Radiomic features were extracted from preoperative apparent diffusion coefficient (ADC) maps derived from multiparametric MRI. Feature selection was performed using appropriate statistical methods, and predictive models were constructed using various machine learning classifiers. The performance of the radiomics signature and a combined model integrating clinical MRI and pathological features were evaluated. Results For pCR prediction, one clinical MRI feature (seminal vesicle invasion), one pathological feature (cribriform adenocarcinoma), and eight radiomics features were ultimately selected. The combined model, which incorporated these features with a radiomics signature generated by a Support Vector Machine (SVM) classifier, demonstrated excellent discriminative ability. It achieved areas under the curve (AUC) of 0.909 (95% CI: 0.852–0.965) in the training cohort and 0.947 (95% CI: 0.882–1.012) in the internal validation cohort. Decision curve analysis confirmed its clinical net benefit. Furthermore, Kaplan-Meier analysis revealed that patients predicted by the model to have a higher probability of pCR experienced significantly better disease-free survival compared to those with a lower predicted probability. Conclusion Based on a substantial sample size and rigorous internal validation, the proposed MRI-based radiomics model achieved a high Radiomics Quality Score (RQS). It shows strong potential for the non-invasive prediction of treatment response and prognosis in HRPCa, demonstrating significant clinical value.