文献类型：期刊文献

英文题名：Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

作者：Li, Jian[1,2];Su, Xin[1,2];Xu, Xiao[1,2];Zhao, Changchun[1,2];Liu, Ang[1,2];Yang, Liwen[1];Song, Baoling[1];Song, Hao[1];Li, Zihan[1];Hao, Xiangyong[2,3]

第一作者：Li, Jian;李俊

通信作者：Hao, XY[1]

机构：[1]Gansu Univ Chinese Med, Gansu Prov Hosp, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[2]Gansu Prov Hosp, Dept Gen Surg, Lanzhou 730000, Peoples R China;[3]Gansu Prov Hosp, Dept Gen Surg, 204 Donggang West Rd, Lanzhou 730000, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Gen Surg, 204 Donggang West Rd, Lanzhou 730000, Peoples R China.

年份：2023

卷号：190

外文期刊名：CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY

收录：;Scopus(收录号：2-s2.0-85168740853);WOS:【SCI-EXPANDED(收录号:WOS:001069535400001)】；

基金：This work was supported by Natural Science Foundation of Gansu Province (Grant No.: 23JRRA1313) , Natural Science Foundation for Young Scientists and the Science & Technology Planning Project of Gansu Province (Grant No.: 18JR3RA058) , Gansu Province Youth Innovative Talents Project (Grant No.: 2021LQGR15) , Health Industry Scientific Research Program of Gansu Province (Grant No.: GSWSKY2020-06) , and Research Projects of Gansu Provincial Hospital (Grant No.: 19SYPYB-7, 18GSSY3-1) .

语种：英文

外文关键词：Microvascular invasion; Hepatocellular carcinoma; Tumor maker; Liquid biopsy; Prediction

摘要：Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.