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.

中文翻译：

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整合术前血液检测结果预测肝细胞癌的微血管侵犯风险


微血管侵犯（MVI）是导致肝细胞癌（HCC）患者预后不良的最重要因素之一，术前检测MVI可极大有利于患者的预后和生存。由于目前仍缺乏有效的术前MVI非侵入性检测策略，因此迫切需要新的MVI测定方法。在本研究中，利用全血细胞计数、血液检测和 AFP 检测结果，基于一种新颖的可解释深度学习方法对 MVI 进行术前预测，以量化 MVI 的风险。所提出的称为“基于解释的风险预测”的方法可以精确估计 MVI 风险，并且与最先进的 MVI 风险估计方法相比，在训练队列和独立验证队列上的一致性指数为 0.9341 和 0.9052，具有更好的性能，分别。此外，对模型输出的进一步分析表明，我们模型中量化的 MVI 风险可以作为 HCC 患者无复发生存率和总生存率的独立术前危险因素。因此，我们的模型在量化 MVI 风险和预测 HCC 患者预后方面显示出巨大潜力。