探索一种基于MRI辐射组学特征训练的最佳机器学习（ML）模型，以区分良性和恶性不可区分的椎体压缩骨折（VCFs）。这项回顾性研究包括在六周内出现背痛（非创伤性）的接受MRI诊断为良性和恶性不可区分的VCFs的患者。两个队列分别从青岛大学附属医院（QUH）和青海红十字医院（QRCH）中回顾性招募。从QUH招募了376位参与者，根据MRI检查日期将其分为训练组（n = 263）和验证组（n = 113）。从QRCH中招募的103名参与者用于评估我们预测模型的外部可推广性。从每个感兴趣区域（ROI）提取了1045个辐射组学特征，用于建立模型。预测模型是基于7个不同的分类器建立的。这些模型表现出良好的功效，可以区分良性和恶性不可区分的VCF。然而，我们的高斯朴素贝叶斯（GNB）模型在验证组中获得了更高的AUC和准确率（0.86，87.61％），优于其他分类器。它还保持了外部测试组的高准确性和敏感性。在本研究中，我们的GNB模型表现优于其他模型，表明它可能更有用于区分不可区分的良性和恶性VCF。•基于MRI的良恶性不可区分VCF的鉴别诊断对于脊柱外科医生或放射科医生来说相当困难。•我们的ML模型提高了鉴别诊断良性和恶性不可区分VCF的诊断效能。•我们的GNB模型对于临床应用具有高准确性和灵敏度。©2023。作者（们）独家许可欧洲放射学会使用。

To explore an optimal machine learning (ML) model trained on MRI-based radiomic features to differentiate benign from malignant indistinguishable vertebral compression fractures (VCFs).This retrospective study included patients within 6 weeks of back pain (non-traumatic) who underwent MRI and were diagnosed with benign and malignant indistinguishable VCFs. The two cohorts were retrospectively recruited from the Affiliated Hospital of Qingdao University (QUH) and Qinghai Red Cross Hospital (QRCH). Three hundred seventy-six participants from QUH were divided into the training (n = 263) and validation (n = 113) cohort based on the date of MRI examination. One hundred three participants from QRCH were used to evaluate the external generalizability of our prediction models. A total of 1045 radiomic features were extracted from each region of interest (ROI) and used to establish the models. The prediction models were established based on 7 different classifiers.These models showed favorable efficacy in differentiating benign from malignant indistinguishable VCFs. However, our Gaussian naïve Bayes (GNB) model attained higher AUC and accuracy (0.86, 87.61%) than the other classifiers in validation cohort. It also remains the high accuracy and sensitivity for the external test cohort.Our GNB model performed better than the other models in the present study, suggesting that it may be more useful for differentiating indistinguishable benign form malignant VCFs.• The differential diagnosis of benign and malignant indistinguishable VCFs based on MRI is rather difficult for spine surgeons or radiologists. • Our ML models facilitate the differential diagnosis of benign and malignant indistinguishable VCFs with improved diagnostic efficacy. • Our GNB model had the high accuracy and sensitivity for clinical application.© 2023. The Author(s), under exclusive licence to European Society of Radiology.