Development and Internal Validation of a Multimodal Prediction Model for Moderate-to-Severe Osteoporotic Vertebral Compression Fractures Using Bone Quality Biomarkers Derived From CT and MRI.

RATIONALE AND OBJECTIVES: To develop and internally validate interpretable prediction models using pre-fracture CT-derived Hounsfield unit (HU) values and MRI-based vertebral bone quality (VBQ) scores to estimate the risk of moderate-to-severe osteoporotic vertebral compression fractures (OVCFs). MATERIALS AND METHODS: This retrospective study included 256 patients with first-onset, single-level OVCFs from 2022 to 2025. Patients were randomly allocated to training (n = 180) and testing (n = 76) sets at a 7:3 ratio. Moderate-to-severe compression was defined as Genant grade ≥ 2. Four prespecified logistic regression models assessed the incremental value of VBQ and HU beyond baseline clinical predictors. Discrimination, calibration, decision curve analysis, threshold behavior, and clinical impact were evaluated. Supplementary machine learning models were benchmarked using the same full predictor set, and the final model was translated into a nomogram. RESULTS: In testing set, model performance improved progressively with added imaging biomarkers. The full multimodal model achieved the highest discrimination, with an AUC of 0.759 and AUPRC of 0.798, outperforming the clinical baseline model (AUC, 0.587; DeLong P = 0.011) and the base + VBQ model (AUC, 0.630; P = 0.014). Calibration assessment showed a Brier score of 0.205 and a non-significant Hosmer-Lemeshow test (P = 0.679). Decision curve analysis showed greater net benefit within clinically relevant threshold ranges. Supplementary machine learning algorithms did not outperform the logistic regression framework. CONCLUSION: A multimodal model incorporating HU and VBQ achieved the best overall performance and was translated into a nomogram to support individualized risk estimation for moderate-to-severe OVCFs.