本研究旨在开发和验证一种基于磁共振成像（MRI）的放射组学标志和临床因素相结合的放射组学图谱，用于区分良性和恶性椎体压缩骨折（VCFs）。 共有189例良性VCFs（n = 112）或恶性VCFs（n = 77）患者被分为训练组（n = 133）和验证组（n = 56）。从MRI T1加权像和短时间TI倒转恢复像中提取放射组学特征以开发放射组学标志，并使用最小绝对收缩和选择算子回归构建Rad分数。使用多变量 logistic 回归分析评估人口统计学和MRI形态学特征以构建临床因素模型。基于Rad分数和独立临床因素构建放射组学图谱。最后，使用接受者操作特征曲线和决策曲线分析（DCA）验证放射组学图谱、临床模型和放射组学标志的诊断性能。 使用六个特征构建了一个组合放射组学模型（组合-RS）。椎弓或后部结构受累、旁肌肿块和液体征被确定为构建临床因素模型的最重要形态学因素。放射组学标志的曲线下面积（AUC）、准确性和特异性表现优于临床模型。将组合-RS、椎弓或后部结构受累、旁肌肿块和液体征相结合的放射组学图谱在训练组和验证组中分别达到了0.92和0.90的良好预测效果。DCA结果显示放射组学图谱具有良好的临床实用性。 基于MRI的放射组学图谱，通过结合放射组学标志和临床因素，在区分良性和恶性VCFs方面展现了良好的预测功效。 版权所有©2023 The Association of University Radiologists. 由Elsevier Inc.出版，保留所有权利。

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.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.