Interpretable machine learning model integrating MRI-derived paraspinal muscle parameters for predicting new vertebral compression fractures after vertebral augmentation
Abstract
OBJECTIVES: To develop and validate interpretable machine learning (ML) models incorporating MRI-derived paraspinal muscle parameters to predict new vertebral compression fractures (NVCF) after vertebral augmentation.
MATERIALS AND METHODS: This multicenter retrospective study included patients with osteoporotic vertebral compression fractures (OVCF) who underwent vertebral augmentation with a 2-year follow-up. Data from two centers were merged and randomly divided (8:2) into training and test sets, while data from the third center served as an independent external validation set. Clinical variables, radiographic parameters, and MRI-derived muscle parameters-including paraspinal muscle fat infiltration (PMFI) and psoas muscle index (PMI)-were analyzed. Variable selection was performed using LASSO and logistic regression. Seven supervised ML algorithms were trained and compared using the area under the curve (AUC), calibration, and decision curve analyses. SHAP values were used to enhance the clinical interpretability of the ML models by providing transparent explanations of model predictions.
RESULTS: A total of 359 patients (median age, 75 years [IQR 69-80]; 77 men) were included, with NVCF occurring in 133 patients (37.0%). Six independent predictors-age, bone mineral density (BMD), PMFI, PMI, kyphotic angle correction rate (KACR), and Genant semiquantitative grade (GSQ)-were identified. The random forest model showed the best performance, with AUCs of 0.963, 0.913, and…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 359 participants or records.
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