Development of a Multimodal Model Combining Imaging and Clinical Data to Predict Bone Cement Leakage During Vertebroplasty for Fresh Thoracolumbar Compression Fractures

ABSTRACT Background Bone cement leakage is a common complication of percutaneous vertebroplasty (PVP), but perioperative risk stratification remains challenging. Existing models based on single‐source clinical or imaging features have limited performance. This study aimed to develop and validate multimodal artificial intelligence (AI) models integrating clinical, imaging, and radiomics features to predict bone cement leakage during PVP. Methods This multicenter study included 486 treated vertebrae from 378 patients at the North, South, and Shenshan campuses of our hospital. The treated vertebra was the prediction unit, while all vertebrae from the same patient were retained in the same dataset partition and cross‐validation fold. Clinical variables, conventional CT imaging features, and CT‐based radiomics features were extracted. Logistic regression identified independent risk factors. Six machine‐learning algorithms were used to construct clinical, imaging, radiomics, and multimodal models. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, F1 score, and decision curve analysis. Independent external and prospective validations were performed, and SHapley Additive exPlanations (SHAP) were used for model interpretation. Results Among the 486 treated vertebrae, 215 (44.2%) had cement leakage. Higher body mass index (BMI), greater cement injection volume, cortical destruction, and posterior wall involvement were independently associated with leakage. Multimodal models generally achieved higher AUCs than the corresponding single‐modality models, with AUCs of 0.902–0.939 in the internal test cohort, 0.722–0.929 in the external validation cohort, and 0.834–0.925 in the prospective validation cohort. Patient‐level aggregation and one‐vertebra‐per‐patient sensitivity analyses yielded broadly comparable discrimination. SHAP identified cortical destruction, cement injection volume, and posterior wall involvement as the most influential predictors. Conclusions The multimodal models demonstrated good and generally consistent discrimination across the internal test, external validation, and prospective validation cohorts and may support perioperative risk assessment and individualized cement injection planning during PVP.