Combining radiomics and imaging biomarkers with clinical variables for the prediction of HCC recurrence after liver transplantation.

IF 3.9 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Liver Transplantation Pub Date : 2025-10-01 Epub Date: 2025-03-18 DOI:10.1097/LVT.0000000000000603
Philipp Schindler, Philippa von Beauvais, Emily Hoffmann, Haluk Morgül, Nikolaus Börner, Max Masthoff, Najib Ben Khaled, Florian Rennebaum, Christian M Lange, Jonel Trebicka, Michael Ingrisch, Michael Köhler, Jens Ricke, Andreas Pascher, Max Seidensticker, Markus Guba, Osman Öcal, Moritz Wildgruber
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引用次数: 0

Abstract

To develop and validate an integrated model that combines CT-based radiomics and imaging biomarkers with clinical variables to predict recurrence and recurrence-free survival in patients with HCC following liver transplantation (LT), this 2-center retrospective study includes 123 patients with HCC who underwent LT between 2007 and 2021. Radiomic features (RFs) were extracted from baseline CT liver tumor volume. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression method with 10-fold cross-validation in the training cohort (n=48) to build a predictive radiomics signature for HCC recurrence. Combined diagnostic models were built based on the radiomics signature supplemented with imaging features beyond the Milan criteria, the AFP (alpha-fetoprotein) model, and Metroticket 2.0 before LT using multivariate logistic regression. Receiver operating characteristic analyses were performed in both internal (n=22) and external (n=53) validation cohorts, and patients were stratified into either high-risk or low-risk groups for HCC recurrence. Kaplan-Meier analysis was performed to analyze recurrence-free survival. LASSO and multivariate regression analysis revealed 4 independent predictors associated with an increased risk of HCC recurrence: radiomics signature of 5 RF, peritumoral enhancement, satellite nodules, and no bridging therapies. For the prediction of tumor recurrence, the highest AUC of the final integrated models combining clinical variables, non-radiomics imaging features, and radiomics was 0.990 and 0.900 for the internal and external validation sets, respectively, outperforming the Milan and clinical stand-alone models. In all integrated models, the high-risk groups had a shorter recurrence-free survival than the corresponding low-risk group. CT-based radiomics and imaging parameters beyond the Milan criteria representing aggressive behavior, along with the history of bridging therapies, show potential for predicting HCC recurrence after LT.

结合放射组学和影像学生物标志物与临床变量预测肝移植后HCC复发。
背景与目的:开发并验证一个综合模型,该模型将基于ct的放射组学和影像学生物标志物与临床变量相结合,以预测肝移植(LT)后HCC患者的复发和无复发生存(RFS)。方法:这项双中心回顾性研究包括123例2007年至2021年间接受肝移植的HCC患者。从基线CT肝肿瘤体积提取放射学特征(RF)。使用最小绝对收缩和选择算子(LASSO)回归方法进行特征选择,并在训练队列(n=48)中进行10倍交叉验证,以建立HCC复发的预测放射组学特征。采用多变量logistic回归,基于放射组学特征补充米兰标准之外的影像学特征、AFP模型和LT前的Metroticket 2.0建立联合诊断模型。在内部(n=22)和外部(n=53)验证队列中进行ROC分析,并将患者分为HCC复发高风险组和低风险组。采用Kaplan-Meier分析RFS。结果:LASSO和多变量回归分析显示了与HCC复发风险增加相关的四个独立预测因素:5种RF的放射组学特征、肿瘤周围增强、卫星结节和无桥接治疗。对于肿瘤复发的预测,最终结合临床变量、非放射组学影像学特征和放射组学的综合模型在内部和外部验证集的最高AUC分别为0.990和0.900,优于米兰模型和临床独立模型。在所有综合模型中,高危组的RFS均短于相应的低危组。结论:基于ct的放射组学和超越米兰标准的影像参数代表了侵袭性行为,以及桥接治疗的历史,显示了预测肝移植后HCC复发的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Liver Transplantation
Liver Transplantation 医学-外科
CiteScore
7.40
自引率
6.50%
发文量
254
审稿时长
3-8 weeks
期刊介绍: Since the first application of liver transplantation in a clinical situation was reported more than twenty years ago, there has been a great deal of growth in this field and more is anticipated. As an official publication of the AASLD, Liver Transplantation delivers current, peer-reviewed articles on liver transplantation, liver surgery, and chronic liver disease — the information necessary to keep abreast of this evolving specialty.
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