Hepatocellular Carcinoma: Retrospective Evaluation of the Correlation Between Gadobenate Dimeglumine-Enhanced Magnetic Resonance Imaging and Pathologic Grade

Current Imaging Diagnosis of Hepatocellular Carcinoma

Simple Summary

The role of imaging in the management of hepatocellular carcinoma (HCC) has significantly evolved and expanded beyond the plain radiological confirmation of the tumor based on the typical appearance in a multiphase contrast-enhanced CT or MRI examination. The introduction of hepatobiliary contrast agents has enabled the diagnosis of hepatocarcinogenesis at earlier stages, while the application of ultrasound contrast agents has drastically upgraded the role of ultrasound in the diagnostic algorithms. Newer quantitative techniques assessing blood perfusion on CT and MRI not only allow earlier diagnosis and confident differentiation from other lesions, but they also provide biomarkers for the evaluation of treatment response. As distinct HCC subtypes are identified, their correlation with specific imaging features holds great promise for estimating tumor aggressiveness and prognosis. This review presents the current role of imaging and underlines its critical role in the successful management of patients with HCC.

Abstract

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer related death worldwide. Radiology has traditionally played a central role in HCC management, ranging from screening of high-risk patients to non-invasive diagnosis, as well as the evaluation of treatment response and post-treatment follow-up. From liver ultrasonography with or without contrast to dynamic multiple phased CT and dynamic MRI with diffusion protocols, great progress has been achieved in the last decade. Throughout the last few years, pathological, biological, genetic, and immune-chemical analyses have revealed several tumoral subtypes with diverse biological behavior, highlighting the need for the re-evaluation of established radiological methods. Considering these changes, novel methods that provide functional and quantitative parameters in addition to morphological information are increasingly incorporated into modern diagnostic protocols for HCC. In this way, differential diagnosis became even more challenging throughout the last few years. Use of liver specific contrast agents, as well as CT/MRI perfusion techniques, seem to not only allow earlier detection and more accurate characterization of HCC lesions, but also make it possible to predict response to treatment and survival. Nevertheless, several limitations and technical considerations still exist. This review will describe and discuss all these imaging modalities and their advances in the imaging of HCC lesions in cirrhotic and non-cirrhotic livers. Sensitivity and specificity rates, method limitations, and technical considerations will be discussed.

Preoperative prediction of pathologic grade of HCC on gadobenate dimeglumine-enhanced dynamic MRI

PURPOSE

To evaluate the value of gadobenate dimeglumine-enhanced MRI in predicting the pathologic grade of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Patients with pathologically proven HCC who underwent preoperative gadobenate dimeglumine-enhanced dynamic MRI were included. Two radiologists blinded to pathology results evaluated images in consensus. Lesions were evaluated quantitatively in terms of ratio of enhancement (RE), and qualitatively based on image features related to tumor aggressiveness. Logistic regression and ROC analyses were used to determine the value of these parameters to predict pathologic grade.

RESULTS

In total, 221 patients (194 males, 27 females, aged 52.9 ± 11.7 years) with 49 poorly differentiated HCCs and 172 well/moderately differentiated HCCs were evaluated. Features significantly related to poorer pathologic grade at univariate analysis included lower RE in the early arterial phase (EAP) (p = 0.001), nonsmooth margins (p = 0.001), absence of capsule (p < 0.001), arterial peritumoral hyperenhancement (p < 0.001), higher AFP (p = 0.004), multiple tumors (p = 0.026), and larger tumor size (p = 0.028). At multivariate analysis, lower RE (EAP) (OR = 0.144, p = 0.002), absence of capsule (OR = 0.281, p = 0.004), and arterial peritumoral hyperenhancement (OR = 4.117, p < 0.001) were independent predictive factors for poorer pathologic grade. ROC analysis showed lower RE (EAP) was predictive of poorer pathologic grade (AUC = 0.667). AUC increased to 0.797 when combined with absence of capsule and presence of peritumoral hyperenhancement.

CONCLUSIONS

Lower RE (EAP), absence of capsule, and arterial peritumoral hyperenhancement were predictive biomarkers for poorer pathologic grade of HCC on gadobenate dimeglumine-enhanced dynamic MRI.

KEY POINTS

• Gadobenate dimeglumine-enhanced dynamic MRI was a useful quantitative biomarker for preoperative prediction of pathologic grade in patients with HCC. • Lower RE in the early arterial phase, absence of capsule, and arterial peritumoral hyperenhancement were potential imaging indicators for preoperative prediction of poorer pathologic grade of HCC on gadobenate dimeglumine-enhanced MRI. • A lower RE in the early arterial phase was effective at predicting poorer pathologic grade of HCCs but prediction is improved when combined with absence of capsule and presence of peritumoral hyperenhancement.

Quantitative analysis of multiphase magnetic resonance images may assist prediction of histopathological grade of small hepatocellular carcinoma

Background

The aim of the study was to investigate whether preoperative quantitative analysis of multiphase magnetic resonance images may assist in predicting the pathological grade of small hepatocellular carcinoma (HCC).

Methods

A total of 49 patients with small HCCs (≤3 cm) underwent multiphase magnetic resonance imaging (MRI) and were retrospectively reviewed. Routine unenhanced and post gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were preoperatively performed. Signal intensity (SI) was measured within the designated region of interest (ROI) including those of the lesion and paraspinous muscles. The lesion-to-paraspinous muscle relative contrast ratio (RCR) on T2-weighted (T2W) imaging, diffusion-weighted (DW) imaging, and dynamic phase Gd-BOPTA-enhanced T1W (T1-weighted) imaging were calculated, and statistical analysis was performed to determine the predictive power for the histological grade.

Results

In all, 49 cases were included comprising 3 well-differentiated (WD) HCCs, 36 moderately differentiated (MD) HCCs, and 10 poorly differentiated (PD) HCCs. There was a negative correlation between the RCR and pathological grade of small HCC in the arterial phase [correlation coefficient (ρ)=-0.305, P<0.05]. However, there was no correlation between RCR in other phases and pathological grade (P>0.05 for all). There was also no correlation between tumor margin, tumor location, cystic/necrotic change, intratumoral fat, enhancement pattern, tumor capsule, tumor boundary or tumor size, and any of the differentiation categories (P>0.05 for all).

Conclusions

The lesion-to-paraspinous muscle RCR on arterial phase Gd-BOPTA-enhanced T1W imaging may be useful for the prediction of the histological characteristics of small HCC.

Small hepatocellular carcinoma: using MRI to predict histological grade and Ki-67 expression

AIMS

To investigate the predictive indicators of small aggressive hepatocellular carcinomas by examining the association between preoperative magnetic resonance imaging (MRI) parameters and Ki-67 expression and histological grade.

MATERIALS AND METHODS

Sixty patients with small hepatocellular carcinomas (tumour diameter: ≤3 cm, tumour numbers: ≤2) who underwent curative resection or biopsy after contrast-enhanced and diffusion-weighted MRI were evaluated retrospectively. Signal intensity (SI) of the whole lesion and erector spinae muscle was measured quantitatively. Tumour-to-muscle SI ratio was calculated. The association between these MRI parameters and histological grade and Ki-67 level was then investigated.

RESULTS

There was a significant correlation between tumour-to-muscle SI ratio and histological grade in tissues captured during the non-enhanced T1-weighted (p=0.001), arterial phase (p=0.001), and portal venous phase (p=0.036) of dynamic contrast-enhanced MRI and apparent diffusion coefficient (p=0.027). Arterial inhomogeneous enhancement was also correlated with high-Ki-67 expression (p=0.032).

CONCLUSIONS

Preoperative MRI may serve as a non-invasive tool for prediction of small, aggressive hepatocellular carcinomas, which may otherwise be treated conservatively.

Noninvasive Evaluation of the Pathologic Grade of Hepatocellular Carcinoma Using MCF-3DCNN: A Pilot Study

Purpose

To evaluate the diagnostic performance of deep learning with a multichannel fusion three-dimensional convolutional neural network (MCF-3DCNN) in the differentiation of the pathologic grades of hepatocellular carcinoma (HCC) based on dynamic contrast-enhanced magnetic resonance images (DCE-MR images).

Methods and Materials

Fifty-one histologically proven HCCs from 42 consecutive patients from January 2015 to September 2017 were included in this retrospective study. Pathologic examinations revealed nine well-differentiated (WD), 35 moderately differentiated (MD), and seven poorly differentiated (PD) HCCs. DCE-MR images with five phases were collected using a 3.0 Tesla MR scanner. The 4D-tensor representation was employed to organize the collected data in one temporal and three spatial dimensions by referring to the phases and 3D scanning slices of the DCE-MR images. A deep learning diagnosis model with MCF-3DCNN was proposed, and the structure of MCF-3DCNN was determined to approximate clinical diagnosis experience by taking into account the significance of the spatial and temporal information from DCE-MR images. Then, MCF-3DCNN was trained based on well-labeled samples of HCC lesions from real patient cases by experienced radiologists. The accuracy when differentiating the pathologic grades of HCC was calculated, and the performance of MCF-3DCNN in lesion diagnosis was assessed. Additionally, the areas under the receiver operating characteristic curves (AUC) for distinguishing WD, MD, and PD HCCs were calculated.

Results

MCF-3DCNN achieved an average accuracy of 0.7396±0.0104 with regard to totally differentiating the pathologic grade of HCC. MCF-3DCNN also achieved the highest diagnostic performance for discriminating WD HCCs from others, with an average AUC, accuracy, sensitivity, and specificity of 0.96, 91.00%, 96.88%, and 89.62%, respectively.

Conclusions

This study indicates that MCF-3DCNN can be a promising technology for evaluating the pathologic grade of HCC based on DCE-MR images.