Machine learning integration of multi-modal radiomics and clinical factors predicts refracture risk after percutaneous kyphoplasty in postmenopausal women.

This study explores the use of radiomic features extracted from preoperative T2-weighted MRI and CT images, combined with machine learning models, to predict the risk of vertebral refracture after percutaneous kyphoplasty (PKP) in postmenopausal women. We retrospectively collect data from 156 postmenopausal women with osteoporotic vertebral compression fractures (OVCFs) who underwent PKP (35 refracture cases, 121 non-refracture controls). All patients had preoperative lumbar T2-weighted MRI and CT scans. We extract MRI and CT radiomic features and constructed radiomic signatures through feature selection. Key clinical factors (age, body mass index [BMI], vertebral CT Hounsfield unit [HU] values, smoking history, diabetes history, alcohol use, etc.) are used to build clinical prediction models. Various machine learning classifiers (Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Random Forest [RF], ExtraTrees, XGBoost, LightGBM, Multi-layer Perceptron [MLP]) are trained on the radiomic signatures and clinical factors. Model performance was evaluated on an independent test set using area under the ROC curve (AUC) as the primary metric. Accuracy, sensitivity, specificity, and other measures on the test set were compared between radiomic models, clinical models, and a combined model. The refracture group (n = 35, 22.4%) is significantly older (72.09 ± 4.25 vs 70.11 ± 3.31 years, P = 0.002) with lower vertebral bone density (97.00 ± 6.31 vs 102.49 ± 4.68 HU, P < 0.001). Among individual algorithms, the KNN clinical model achieves optimal performance (AUC = 0.74), while the SVM radiomics model demonstrates the best accuracy (AUC = 0.798, accuracy = 0.839, sensitivity = 0.857, specificity = 0.833). The combined model achieves superior performance (AUC = 0.886), significantly outperforming both standalone models. Multi-modal radiomics combined with key clinical factors provides superior prediction of refracture risk after PKP. This approach offers clinicians an objective tool for individualized risk stratification, representing a meaningful step toward precision medicine in managing osteoporotic fractures.