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

医学 无线电技术 逻辑回归 肝移植 Lasso(编程语言) 比例危险模型 米兰标准 特征选择 接收机工作特性 多元统计 回顾性队列研究 内科学 肿瘤科 多元分析 队列 放射科 移植 人工智能 机器学习 万维网 计算机科学
作者
Philipp Schindler,P Beauvais,Emily Hoffmann,Haluk Morgül,Nikolaus Börner,Max Masthoff,Najib Ben Khaled,Florian Rennebaum,Christian Lange,Jonel Trebicka,Michael Ingrisch,Michael Köhler,Jens Ricke,Andreas Pascher,Max Seidensticker,Markus Guba,Osman Öcal,Moritz Wildgruber
出处
期刊:Liver Transplantation [Lippincott Williams & Wilkins]
卷期号:31 (10): 1226-1237 被引量:11
标识
DOI:10.1097/lvt.0000000000000603
摘要

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.
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