Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma

Abstract

Background: Prediction models for the histological grade of hepatocellular carcinoma (HCC) remain unsatisfactory. The purpose of this study is to develop preoperative models to predict histological grade of HCC based on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) radiomics. And to compare the performance between artificial neural network (ANN) and logistic regression model. Methods: A total of 122 HCCs were randomly assigned to the training set (n=85) and the test set (n=37). There were 242 radiomic features extracted from volumetric of interest (VOI) of arterial and hepatobiliary phases images. The radiomic features and clinical parameters [gender, age, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), alanine aminotransferase (ALT), aspartate transaminase (AST)] were selected by permutation test and decision tree. ANN of arterial phase (ANN-AP), logistic regression model of arterial phase (LR-AP), ANN of hepatobiliary phase (ANN-HBP), logistic regression mode of hepatobiliary phase (LR-HBP), ANN of combined arterial and hepatobiliary phases (ANN-AP+HBP), and logistic regression model of combined arterial and hepatobiliary phases (LR-AP+HBP) were built to predict HCC histological grade. Those prediction models were assessed and compared. Results: ANN-AP and LR-AP were composed by AST and radiomic features based on arterial phase. ANN-HBP and LR-HBP were composed by AFP and radiomic features based on hepatobiliary phase. ANN-AP+HBP and LR-AP+HBP were composed by AST and radiomic features based on arterial and hepatobiliary phases. The prediction models could distinguish between high-grade tumors [Edmondson-Steiner (E-S) grade III and IV] and low-grade tumors (E-S grade I and II) in both training set and test set. In the test set,

Current status and future perspectives of radiomics in hepatocellular carcinoma

“…Wu et al [ 30 ] found that MRI-based radiomics can successfully categorize low-grade and high-grade HCC, with the radiomic model outperforming the clinical model (AUC 0.742 for the combined T1-weighted and T2-weighted MRI-based radiomic model vs AUC 0.6 for the clinical one) and the combined radiomic and clinical model (AUC 0.8) outperforming both models alone. Mao et al [ 31 ] also investigated MRI-based radiomic features, with Gd-EOB-DTPA contrast administered for the MRI exams, finding that the artificial neural network combining radiomic features from the contrast-enhanced arterial phase and hepatobiliary phase yielded the highest AUC of 0.944. Moreover, they found that the artificial neural network models were superior to the logistic regression models.…”

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

“…In this study, 3D slicer (version 4.11.20210226, https://www.slicer.org/ ) image segmentation software was used to delineate the region of interest (ROI) of GP-NENs masses, including plain scan, arterial phase, and venous phase CT data, and followed by texture analysis and data extraction. After determining the candidate texture data such as firstorder, glcm, and ngtdm, R (version 4.1.3) was then used to perform cross-validation and Lasso coefficients regression on the above texture data to extract valid texture data and generate radiomic score (Radscore) values [ 12 , 13 ].…”

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

“…30 Several studies have constructed radiomics models using HBP radiomics features that could predict tumor differentiation grade. 37,38 Wu et al found that a radiomics signature based on T1-weighted imaging and T2-weighted imaging can successfully categorize low-grade and high-grade HCC. 39 MTM-HCC has received significant attention in recent years, which is an aggressive subtype associated with angiogenesis and immunosuppressive tumor microenvironment.…”

“…The hepatobiliary phase (HBP) of DCE‐MRI with Gd‐EOB‐DTPA is particularly useful for evaluating liver tumors, as it has been shown to provide important information about tumor aggressiveness 30 . Several studies have constructed radiomics models using HBP radiomics features that could predict tumor differentiation grade 37,38 . Wu et al found that a radiomics signature based on T1‐weighted imaging and T2‐weighted imaging can successfully categorize low‐grade and high‐grade HCC 39 .…”