Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer - Jiang, Minghan; Miao, Zeyang; Xu, Run; Guo, Mengyao; Li, Xuefeng; Li, Guanwu; Luo, Peng; Hu, Su (Frontiers in Oncology, 2025)
ObjectiveThis study aimed to develop MRI-based radiomics machine learning models for predicting adverse pathological prognostic features in prostate cancer and to explore the feasibility of integrating radiomics with clinical characteristics to improve preoperative risk stratification, addressing the limitations of conventional clinical models.MethodsA retrospective cohort of 137 prostate cancer patients between January 2021 and April 2023 with preoperative MRI and postoperative pathology data was divided into adverse-feature-positive (n=85) and negative (n=52) groups. Regions of interest (ROIs) were delineated on ADC and T2WI sequences, and 31 radiomics features were extracted using PyRadiomics. LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). Clinical models incorporated three variables: biopsy Gleason grade, total PSA, and prostate volume. The best-performing radiomics model was combined with clinical features to build a hybrid model. Model performance was evaluated by AUC, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).ResultsPatients were randomly split into training (n=95) and validation (n=42) cohorts. The random forest model using ADC-T2WI combined features achieved the highest AUC (0.832; 95% CI: 0.706–0.958) in the validation set, outperforming the clinical model (AUC=0.772). The hybrid model demonstrated superior performance (AUC=0.909; 95% CI: 0.822–0.995), with sensitivity=0.813, specificity=0.885, and accuracy=0.857. Calibration and DCA confirmed its robust clinical utility (p<0.01 vs. single models).ConclusionsThe biparametric MRI radiomics-random forest model effectively predicts adverse pathological features in prostate cancer. Integration with clinical characteristics further enhances predictive accuracy, offering a non-invasive tool for preoperative risk stratification and personalized treatment planning.