由于对影像征象的不一致解释，预测肝细胞癌（HCC）患者微血管侵犯（MVI）的成像模型的再现性仍存在疑问。我们的目的是筛选高一致性的MRI特征，以开发用于预测MVI的可重复模型。我们纳入了接受手术切除的219例HCC患者，并将患者分为训练组（n=145）和验证组（n=74）。形态特征、肝胆相位信号特征和动态增强模式的定性观察进行了观察员间的一致性评估。使用Cohen's κ评估观察员间的一致性，以选择具有高一致性的特征。具有显著因素并能够获得良好观察者间一致性的风险因素被用于构建预测模型，并在验证组中进行评估。模型的诊断性能基于受试者工作特征曲线下面积（AUC）进行评估。多元分析确定了非光滑肿瘤边缘、放射学包膜缺失和肿瘤内动脉是MVI的独立危险因素。这些基于MRI的特征在放射科医师间表现出良好或几乎完美的一致性（κ > 0.6）。预测模型在训练组（AUC 0.734）和验证组（AUC 0.759）中预测了MVI，并适合校准曲线。MRI特征包括非光滑肿瘤边缘、放射学包膜缺失和肿瘤内动脉，能够在放射科医师间获得高一致性，并能够预测HCC患者的MVI。此处描述的预测模型可能对放射科医师有用，无论其经验水平如何。版权所有©2023年大学放射科医师协会。由Elsevier Inc.出版。保留所有权利。

The reproducibility of imaging models for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains questionable due to inconsistent interpretation of image signs. Our aim was to screen for high-consensus MRI features to develop a repeatable model for predicting MVI.We included 219 patients with HCC who underwent surgical resection, and patients were divided into a training cohort (n = 145) and a validation cohort (n = 74). Morphological characteristics, signal features on hepatobiliary phases, and dynamic enhancement patterns were qualitatively interobserver evaluated. Interobserver agreement was assessed using Cohen's κ for selecting features with high interobserver agreement. Risk factors that were significant in stepwise multivariate analysis and that could be measured with good interobserver agreement were used to construct a predictive model, which was assessed in the validation cohort. The diagnostic performance of the model was evaluated based on area under the receiver operating characteristic curve (AUC).Multivariate analysis identified nonsmooth tumor margin, absence of radiologic capsule, and intratumoral artery as independent risk factors of MVI. These MRI-based features showed good or nearly perfect interobserver agreement between radiologists (κ > 0.6). The predictive model predicted MVI well in the training (AUC 0.734) and validation cohorts (AUC 0.759) and fitted well to calibration curves.MRI features included nonsmooth tumor margin, absence of radiologic capsule, and intratumoral artery that can be assessed with high interobserver agreement can predict MVI in HCC patients. The predictive model described here may be useful to radiologists, regardless of experience level.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.