Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters

Abstract

Purpose 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 signi cant 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.Methods HCC patients with pathologically con rmed 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.Results 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 strati cation, 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. ConclusionThe 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.

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

“…The systematic literature search initially yielded 188 records from the four databases. After removing 82 duplicates, 50 inappropriate types of publications, and 34 ineligible studies, a total of 22 studies were included in this systematic review [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] ( Figure 2 ).…”

Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma

“…Song et al. ( 35 ) and Wei et al. ( 36 ) used deep learning to predict MVI in HCC based on MRI with an accuracy of 79.3 and 81.2%, respectively.…”

Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

“…Despite AI's promise for translation in liver imaging, discrepancies in methodology prevent incorporation into clinical decision making. Considerable variation exists within the radiomics workflow starting from data acquisition to final selection of features [58,83], although similar considerations apply to deep learning. In liver imaging, CT, MRI, or ultrasound constitute imaging modalities with distinct data acquisition parameters.…”