Abstract
Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient's prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters. HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction. Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients. The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient's prognosis.
Highlights
Hepatocellular carcinoma (HCC) is ranked as the sixth in terms of incidence cases and the fourth with respect to the cause of cancer-related death worldwide (Villanueva 2019)
To predict the presence of Microvascular invasion (MVI), the deep learning (DL) model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731
The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931
Summary
Hepatocellular carcinoma (HCC) is ranked as the sixth in terms of incidence cases and the fourth with respect to the cause of cancer-related death worldwide (Villanueva 2019). Surgical treatment is regarded as the major curative treatment for patients with HCC (Ishizawa et al 2008; Zhou et al 2018). Postoperative recurrence and metastasis remain major obstacles for the prognosis of HCC patients (Han et al 2015; Hasegawa et al 2013). The presence of MVI is considered as an independent prognostic factor associated with the early recurrence and poor survival after both resection and transplantation (Lim et al 2011; Mazzaferro et al 2009a; Mazzaferro et al 2009b; Vitale et al 2014). For the MVI-positive patients, hepatectomy with expanding resection margins can significantly improve patient survival through eradicating micro-metastases (Han et al 2019; Shindoh et al 2013; Tsilimigras et al 2020). Surgical resection rather than radiofrequency ablation (RFA)
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