An MRI-Based Radiomics Nomogram for Differentiation of Benign and Malignant Vertebral Compression Fracture

This study aimed to develop and validate a magnetic resonance imaging (MRI)-based radiomics nomogram combining radiomics signatures and clinical factors to differentiate between benign and malignant vertebral compression fractures (VCFs).

A total of 189 patients with benign VCFs (n = 112) or malignant VCFs (n = 77) were divided into training (n = 133) and validation (n = 56) cohorts. Radiomics features were extracted from MRI T1-weighted images and short-TI inversion recovery images to develop the radiomics signature, and the Rad score was constructed using least absolute shrinkage and selection operator regression. Demographic and MRI morphological characteristics were assessed to build a clinical factor model using multivariate logistic regression analysis. A radiomics nomogram was constructed based on the Rad score and independent clinical factors. Finally, the diagnostic performance of the radiomics nomogram, clinical model, and radiomics signature was validated using receiver operating characteristic and decision curve analysis (DCA).

Six features were used to build a combined radiomics model (combined-RS). Pedicle or posterior element involvement, paraspinal mass, and fluid sign were identified as the most important morphological factors for building the clinical factor model. The radiomics signature was superior to the clinical model in terms of the area under the curve (AUC), accuracy, and specificity. The radiomics nomogram integrating the combined-RS, pedicle or posterior element involvement, paraspinal mass, and fluid sign achieved favorable predictive efficacy, generating AUCs of 0.92 and 0.90 in the training and validation cohorts, respectively. The DCA indicated good clinical usefulness of the radiomics nomogram.

The MRI-based radiomics nomogram, combining the radiomics signature and clinical factors, showed favorable predictive efficacy for differentiating benign from malignant VCFs.