Predicting Conservative Treatment Failure in Postmenopausal Women With Osteoporotic Vertebral Compression Fractures: A CT and MRI-Based Radiomics Machine Learning Approach.

Study designRetrospective study.ObjectiveOsteoporotic vertebral compression fractures (OVCF) affect postmenopausal women, with 30-40% requiring surgical intervention after conservative treatment failure. This study developed a CT and MRI radiomics-based model to predict conservative treatment failure risk.MethodsWe retrospectively analyzed 154 postmenopausal women with OVCF (2016-2024), divided into successful (n = 86) and failed (n = 68) conservative treatment groups. Three-dimensional regions of interest were delineated, and quantitative features extracted using PyRadiomics. Feature selection employed Mann-Whitney U test, Spearman correlation, and LASSO regression. Clinical, radiomics, and combined models were constructed using eight machine learning algorithms with 5-fold cross-validation.ResultsAge and vertebral CT Hounsfield units were significant clinical predictors. From 3668 initial features, 16 key radiomics features were selected. LightGBM performed best for clinical models, while k-nearest neighbors excelled for radiomics models. In testing, the clinical model achieved AUC 0.684 (accuracy 0.71), radiomics model AUC 0.812 (accuracy 0.71), and combined model AUC 0.859 (accuracy 0.806). The combined model significantly outperformed individual models.ConclusionThe comprehensive CT and MRI radiomics-based model accurately predicts conservative treatment failure risk in postmenopausal women with OVCF. This tool enables early identification of high-risk patients and supports individualized treatment decisions, potentially guiding early surgical intervention for predicted high-risk cases.