增殖性肝细胞癌（HCC）具有高侵袭性和不良预后。本研究旨在探讨不同放射组学模型和列线图对增殖性 HCC 的术前风险预测和预后价值。将患者按 7:3 随机分为训练队列 (n = 156) 和验证队列 (n = 66)比率。从 T1 加权成像 (T1WI)、动脉和肝胆阶段提取原始和 delta（从两个不同阶段提取的成像特征之间的不同值）放射组学特征，以使用不同的机器学习算法构建模型。使用逻辑回归来选择临床独立危险因素。通过将最佳放射组学模型评分与独立风险因素相结合来构建列线图。评估了模型的诊断功效和临床实用性。随后，根据放射组学模型评分和列线图评分将患者分为高危和低危亚组，并评估无复发生存期（RFS）和总生存期（OS）。多因素logistic回归分析显示，BCLC分期和联合 radscore 是增殖性 HCC 的独立预测因子。训练组和验证组中包含这些因素的列线图的曲线下面积 (AUC) 分别为 0.838 和 0.801，具有良好的预测性能。多变量 Cox 回归分析显示，delta 放射组学模型 (DR) 预测的增殖性 HCC 可以独立预测 RFS 和 OS，其中 delta 放射组学模型的分数在预后风险分层中表现最佳。列线图可以有效预测增殖性 HCC，而不同的放射组学模型列线图可以提供不同的预后分层值。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

Proliferative hepatocellular carcinoma (HCC) is associated with high invasiveness and poor prognosis. This study aimed to investigate the preoperative risk prediction and prognostic value of different radiomics models and a nomogram for proliferative HCC.Patients were randomly divided into a training cohort (n = 156) and a validation cohort (n = 66) in a 7:3 ratio. Original and delta (the different value between imaging features extracted from two different phases) radiomics features were extracted from T1-weighted imaging (T1WI), arterial, and hepatobiliary phases to construct models using different machine learning algorithms. Logistic regression was used to select clinical independent risk factors. A nomogram was constructed by integrating the optimal radiomics model score with independent risk factors. The diagnostic efficacy and clinical utility of the models were assessed. Subsequently, patients were stratified into high-risk and low-risk subgroups based on radiomics model scores and nomogram scores, and both recurrence-free survival (RFS) and overall survival (OS) were evaluated.Multivariate logistic regression analysis showed that BCLC stage and combined radscore were independent predictors of proliferative HCC. The area under the curve (AUC) of the nomogram incorporating these factors was 0.838 and 0.801 in the training and validation cohorts, respectively, with good predictive performance. Multivariate Cox regression analysis shows that the delta radiomics model (DR)-predicted proliferative HCC can independently predict RFS and OS, with scores from the delta radiomics model performing best in prognostic risk stratification.The nomogram can effectively predict proliferative HCC, while different radiomics models and the nomogram can offer varying prognostic stratification values.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.