Deep Learning Radiomics Model of Dynamic Contrast‐Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma

Abstract

BackgroundVessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging.PurposeTo develop and validate a deep learning radiomic (DLR) model of dynamic contrast‐enhanced MRI (DCE‐MRI) for the preoperative discrimination of VETC and prognosis of HCC.Study typeRetrospective.PopulationA total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time‐independent validation set (n = 67).Field Strength/SequenceA 1.5 T and 3.0 T; DCE imaging with T1‐weighted three‐dimensional fast spoiled gradient echo.AssessmentHistological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC−. The regions of intratumor and peritumor were segmented manually in the arterial, portal‐venous and delayed phase (AP, PP, and DP, respectively) of DCE‐MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical–radiological (CR) models based on AP, PP, and DP of DCE‐MRI for evaluating VETC status and association with recurrence.Statistical TestsThe Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan–Meier survival analysis. P value <0.05 was considered as statistical significance.ResultsPathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri‐PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri‐PP DLR model‐predicted VETC+ and VETC− status were found.Data ConclusionsThe DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively.Evidence Level4.Technical EfficacyStage 2.

MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges

“…Regarding VETC, Dong et al conducted a multichannel deep learning approach with a well-predictive AUC of 0.84 to predict VETC. 71 As the specific effect of the label combination of MVI and VETC in predicting prognosis remains unclear, 3D CNN for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of both tasks have shown improved performance, with an AUC of 0.92. 72 Some of the most valuable biomarkers used in the prognosis of HCC are CK19 and Ki-67.…”

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography

“…Radiomics has found use in HCC, including preoperative prediction of pathological indicators[ 17 ], differential diagnosis[ 18 ], evaluating curative effect, and prognosis prediction[ 19 ]. Recently, Yu et al [ 20 ] applied gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (MRI) radiomics approach to evaluate VETC in HCC, Dong et al [ 21 ] attempted to develop deep learning radiomics model of dynamic contrast-enhanced MRI to predict VETC in HCC. As a routine examination method, the emergence of computed tomography (CT) has made a qualitative leap in the imaging diagnosis of liver cancer and driven the progress of liver surgery.…”