August 5, 2025Open Access

Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer

MJM Z JiangZhejiang University ZMZeyang MiaoShanghai University of Traditional Chinese Medicine RXRun XuMianyang Third People's Hospital

Key Points

The hybrid model combining radiomics and clinical features achieved the highest AUC of 0.909 in predicting adverse pathological features.
Random forest emerged as the best-performing algorithm, significantly outperforming conventional clinical models.
A cohort of 137 prostate cancer patients was analyzed, revealing the effectiveness of MRI-based machine learning in stratification.
Integration of clinical characteristics with radiomics offers a non-invasive method for personalized treatment planning.

Abstract

Objective This 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. Methods A 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). Results Patients 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). Conclusions The 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.

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