文章基本信息

标题：Integration of pre-surgical blood test results predict microvascular invasion risk in hepatocellular carcinoma
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作者：Geng Chen ; Rendong Wang ; Chen Zhang 等
期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2021
卷号：19
页码：826-834
DOI：10.1016/j.csbj.2021.01.014
出版社：Computational and Structural Biotechnology Journal
摘要：Microvascular invasion (MVI) is one of the most important factors leading to poor prognosis for hepatocellular carcinoma (HCC) patients, and detection of MVI prior to surgical operation could great benefit patient’s prognosis and survival. Since it is still lacking effective non-invasive strategy for MVI detection before surgery, novel MVI determination approaches were in urgent need. In this study, complete blood count, blood test and AFP test results are utilized to perform preoperative prediction of MVI based on a novel interpretable deep learning method to quantify the risk of MVI. The proposed method termed as “Interpretation based Risk Prediction” can estimate the MVI risk precisely and achieve better performance compared with the state-of-art MVI risk estimation methods with concordance indexes of 0.9341 and 0.9052 on the training cohort and the independent validation cohort, respectively. Moreover, further analyses of the model outputs demonstrate that the quantified risk of MVI from our model could serve as an independent preoperative risk factor for both recurrence-free survival and overall survival of HCC patients. Thus, our model showed great potential in quantification of MVI risk and prediction of prognosis for HCC patients.
关键词：Hepatocellular carcinoma ; Microvascular invasion ; Deep learning ; Interpretation of machine learning ; Blood test