A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria

Translational Oncology

卷:14

Junli Chen¹  Zhikun Liu²  Siyi Dong³  Xiao Xu³  Wenhui Zhang³  Beini Cen⁴  Shusen Zheng⁴ 

[1] Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China;

[2] Zhejiang University Cancer center, Hangzhou, 310058, China;

[3] Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China;

[4] National Center for healthcare quality management in liver transplant, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China;

关键词: Microvascular Invasion (MVI);    Liver Transplantation (LT);    Hepatocellular Carcinoma (HCC);   

DOI  :  

来源: DOAJ

【 摘 要 】

Purpose: The prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients. Methods: In total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets. Results: Univariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC. Conclusions: The nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.

【 授权许可】

Unknown