基于Gd-EOB-DTPA增强MRI中的影像信息特征，开发和验证一种基于随机森林模型的方法，用于预测单发HCC中Ki-67表达情况。本回顾性研究分析了258例单发HCC患者。通过单变量和多变量分析确定了显著的临床和影像学因素，用于区分高（>20%）和低（≤20%）Ki-67表达的HCC。提取了Gd-EOB-DTPA增强MRI的影像信息特征。采用递归特征消除（RFE）策略筛选出稳定的影像信息特征，并利用随机森林（RF）算法对影像信息特征进行排序和构建预测模型。使用AUC、准确率、精确率、召回率和F1-score评估RF模型的性能。多变量分析确定血清AFP水平、肿瘤大小、生长类型和周围肿瘤增强为HCC高Ki-67表达的独立预测因子。将临床和影像学预测因子与前十个影像信息特征结合的临床影像学-影像模型在训练集中表现出优于临床影像学模型的性能（AUC分别为0.876和0.780，p<0.001），然而在测试集中并无统计学意义（AUCs 0.809 vs. 0.723，p=0.123）。在两个数据集中，加入临床和影像学预测因子并未显著改善影像信息特征的性能（训练集，p=0.692；测试集，p=0.229）。决策曲线分析进一步证实了RF模型的临床实用性。基于Gd-EOB-DTPA增强MRI的影像信息特征，RF模型在术前预测HCC中Ki-67表达方面取得了令人满意的性能。版权所有 © 2023年大学放射学协会。由Elsevier Inc.发表，保留所有权利。

To develop and validate a random forest model based on radiomic features in Gd-EOB-DTPA enhanced MRI for predicting the Ki-67 expression in solitary HCC.This retrospective study analyzed 258 patients with solitary HCC. Significant clinicoradiological factors were identified through univariate and multivariate analyses for distinguishing HCC with high (>20%) and low (≤20%) Ki-67 expression. Radiomic features were extracted at Gd-EOB-DTPA enhanced MRI. The recursive feature elimination (RFE) strategy was employed to screen robust radiomic features, and the Random Forest (RF) algorithm was utilized to rank radiomic features and construct prediction models. The AUC, accuracy, precision, recall, and f1-score were used to evaluate the performance of RF models.Multivariate analysis identified serum AFP level, tumor size, growth type, and peritumoral enhancement as independent predictors for HCC with high Ki-67 expression. The clinicoradiological-radiomic model that incorporated the clinicoradiological predictors and the top ten radiomic features outperformed the clinicoradiological model in the training set (AUCs 0.876 vs. 0.780; p < 0.001), though the test set did not have a statistical significance (AUCs 0.809 vs. 0.723; p = 0.123). The addition of clinicoradiological predictors did not yield a significant improvement in the performance of radiomic features in both sets (training, p = 0.692; test, p = 0.229). Decision curve analysis further confirmed the clinical utility of the RF models.The RF models based on radiomic features of Gd-EOB-DTPA enhanced MRI achieved satisfactory performance in preoperatively predicting Ki-67 expression in HCC.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.