基于Gadoxetic Acid增强MRI的放射组学模型用于肝细胞癌的术前风险预测和预后评估

Gadoxetic Acid-Enhanced MRI-Based Radiomic Models for Preoperative Risk Prediction and Prognostic Assessment of Proliferative Hepatocellular Carcinoma

ACADEMIC RADIOLOGY

影响因子:3.9

分区:医学2区 / 核医学2区

发表日期:2025 Jan

作者: Zuyi Yan, Zixin Liu, Guodong Zhu, Mengtian Lu, Jiyun Zhang, Maotong Liu, Jifeng Jiang, Chunyan Gu, Xiaomeng Wu, Tao Zhang, Xueqin Zhang

DOI: 10.1016/j.acra.2024.07.040

摘要

增殖性肝细胞癌（HCC）具有高度侵袭性和预后不良的特点。本研究旨在探讨不同放射组学模型及列线图在增殖性HCC术前风险预测和预后中的价值。患者被随机分为训练组（n=156）和验证组（n=66），比例为7:3。从T1加权成像（T1WI）、动脉期和肝胆期提取原始和变化（不同相位的影像特征之间的差异）放射组学特征，使用不同机器学习算法构建模型。采用逻辑回归筛选临床独立危险因素。通过整合最佳放射组学模型评分与独立危险因素，建立了列线图。评估模型的诊断效能和临床应用价值。随后，根据放射组学模型评分和列线图评分，将患者分为高风险和低风险亚组，评估无复发生存期（RFS）和总生存期（OS）。多变量逻辑回归分析显示，BCLC分期和联合放射评分是增殖性HCC的独立预测因子。包含这些因素的列线图的曲线下面积（AUC）在训练组为0.838，在验证组为0.801，表现良好。多变量Cox回归分析表明，变化放射组学模型（DR）预测的增殖性HCC可以独立预测RFS和OS，且变化放射组学模型在预后风险分层中表现最佳。该列线图可以有效预测增殖性HCC，而不同的放射组学模型和列线图能提供不同的预后分层价值。

Abstract

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.