Efficacy of texture analysis of pre-operative magnetic resonance imaging in predicting microvascular invasion in hepatocellular carcinoma

Abstract

BACKGROUND Presence of microvascular invasion (MVI) indicates poorer prognosis post-curative resection of hepatocellular carcinoma (HCC), with an increased chance of tumour recurrence. By present standards, MVI can only be diagnosed post-operatively on histopathology. Texture analysis potentially allows identification of patients who are considered ‘high risk’ through analysis of pre-operative magnetic resonance imaging (MRI) studies. This will allow for better patient selection, improved individualised therapy (such as extended surgical margins or adjuvant therapy) and pre-operative prognostication. AIM This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC. METHODS Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed. Exclusion criteria: No pre-operative MRI, significant movement artefacts, loss-to-follow-up, ruptured HCCs, previous hepatectomy and adjuvant therapy. Fifty patients were divided into MVI ( n = 15) and non-MVI ( n = 35) groups based on tumour histology. Selected images of the tumour on post-contrast-enhanced T1-weighted MRI were analysed. Both qualitative (performed by radiologists) and quantitative data (performed by software) were obtained. Radiomics texture parameters were extracted based on the largest cross-sectional area of each tumor and analysed using MaZda software. Five separate methods were performed. Methods 1, 2 and 3 exclusively made use of features derived from arterial, portovenous and equilibrium phases respectively. Methods 4 and 5 made use of the comparatively significant features to attain optimal performance. RESULTS Method 5 achieved the highest accuracy of 87.8% with sensitivity of 73% and specificity of 94%. CONCLUSION Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8% and can potentially impact clinical management.

The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study

“…Calculating parametric feature maps might have been beneficial in their study since variance, skewness, kurtosis, and entropy showed increased reproducibility across VOI sizes when extracted from the feature maps (yet, mean is also reproducible when derived conventionally from the original images). Another group [ 40 ] analyzed links between textural features and microvascular invasion in hepatocellular carcinoma in post-contrast-enhanced T1-weighted images. Resected specimens served as the reference standard.…”

Prognosis Prediction of Hepatocellular Carcinoma Based on Magnetic Resonance Imaging Features

“…Xiong et al [ 37 ] developed a prediction model based on preoperative AFP, tumor diameter, and TNM stage with an area under the receiver operating characteristic curve of 0.80 and good practicability. Texture analysis of tumor nodules could improve the diagnosis of MVI over visual analysis by human readers [ 38 ] and harbors the potential for deep learning algorithm development. Unfortunately, owing to the numerous types of image features, most studies have used different classification features and weights to predict MVI.…”

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

“…Occasionally, literature has demonstrated that high cell proliferation index KI-67 [9], mutation of tumor suppressor gene P53 [10] and other factors are correlated with poor prognosis. In addition, incomplete tumor capsule [11], MVI [12,13], envelope invasion (EI) [14], vascular invasion (VI) and SN [15,16] often represent poor prognosis. However, the thickness of hepatic plates (HP), vessels encapsulating tumor clusters (VETC) [17] and some other novel biomarkers, which were recently found to correlate with prognosis and/or treatment, were seldomly included in the pathological diagnosis report.…”