基于磁共振成像（MRI）的肝脏恶性和良性病变的区别诊断是一项重要但常常具有挑战性的任务，尤其是在非肝硬化的肝脏中。我们开发并进行了外部验证，使用放射组学模型定量评估非肝硬化肝脏中最常见的恶性和良性原发性实质性肝病变的T2加权MR成像。数据集于2002年至2018年间从三个三级转诊中心（A、B和C）进行了回顾性收集。纳入了病人的病理为恶性（肝细胞癌和肝内胆管癌）和良性（肝细胞腺瘤和局灶性结节性增生）病变。我们在数据集A上使用机器学习方法的组合开发了基于T2加权MR成像的放射组学模型。该模型在数据集A上通过交叉验证进行了内部评估，在数据集B和C上进行了外部验证，并与两位经验丰富的腹部放射学家对数据集C进行了视觉评分比较。整个数据集包括486名患者（A：187、B：98和C：201）。该放射组学模型在数据集A上的平均曲线下面积（AUC）为0.78，在外部验证中具有相似的AUC（B：0.74和C：0.76）。在数据集C中，两位放射科医生显示了适度一致性（Cohen's κ：0.61），并且其AUC分别为0.86和0.82。我们的T2加权MR成像放射组学模型显示了区分恶性和良性原发性肝脏实质性病变的潜力。外部验证表明，尽管存在相当大的MRI采集协议差异，该模型具有可泛化性。在进一步的优化和泛化之前，该模型可能有助于放射科医生改善肝脏病变的诊断工作。版权所有©2023年美国大学放射科医师协会。由Elsevier Inc.出版。

Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers.Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C.The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82.Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.