Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures Based on CT Radiomics Model

Xinrui Liu^1,2Song Chen³Yifan Wang⁴Jiashi Cao^5,6Zhuangfei Niu²Yuxian Jin⁴Xingdan Pan^1,2Zhengwei Zhang^1,2Tielong Liu^1,5Wei Liang^7*Panfeng Yu^8*Weiwei Zou^1,2*

• ¹University of Shanghai for Science and Technology, Shanghai, China
• ²Shanghai Changzheng Hospital Department of Radiology, Shanghai, China
• ³Shanghai Baoshan District Wusong Central Hospital (Zhongshan Hospital Wusong Branch, Fudan University), Shanghai, China
• ⁴Shanghai Changzheng Hospital, Shanghai, China
• ⁵Shanghai Changzheng Hospital Department of Orthopedic Oncology, Shanghai, China
• ⁶Navy Medical Center, the Navy Medical University, Shanghai, China
• ⁷Shanghai 411 Hospital, Affiliated Hospital of Shanghai University, Shanghai, China
• ⁸Peking University People's Hospital, Beijing, China

Objectives: This study aims to develop a CT radiomics-based predictive model integrating clinical characteristics to distinguish benign and malignant vertebral compression fractures (VCFs). Methods: We retrospectively analyzed 208 patients with VCFs treated at our institution between January 2020 and November 2024. Patients were randomly divided into a training cohort (n = 145) and a validation cohort (n = 63). CT images were obtained, and three-dimensional lesion regions were manually segmented. A total of 1,316 radiomics features were extracted. Dimensionality reduction was performed using least absolute shrinkage and selection operator (LASSO) regression analysis and 5-fold cross-validation to identify key features. Univariate and multivariate analyses were used for identifying independent clinical predictors. Three models were constructed: a clinical model, a radiomics model, and a combined clinical–radiomics model. Model performance was evaluated using area under the receiver operating characteristic (ROC) curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV). Predictive efficacy and clinical utility were further assessed via ROC curves, calibration plots, and decision curve analysis (DCA), along with clinical impact curves (CIC) and net reduction curves. The Delong test was used for statistical comparisons among different models, and a nomogram was developed to facilitate the visualization of the optimal model. Results: Carbohydrate antigen 125 (CA125) and posterior vertebral involvement were identified as independent clinical predictors. The combined model achieved the highest AUC value of 0.846 in the validation cohort, followed by the radiomics model (0.842), and the clinical model (0.640). Calibration curves and DCA confirmed its superior predictive accuracy and clinical benefit. Conclusions: The CT-based clinical–radiomics model demonstrated robust performance in differentiating benign from malignant VCFs and holds promise for guiding individualized patient management.