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Hepatocellular carcinoma (HCC) is a unique tumor because it is one of the few cancers which can be treated based on imaging alone. Magnetic resonance imaging (MRI) carries higher sensitivity and specificity for the diagnosis of HCC than either computed tomography (CT) or ultrasound. MRI is imaging modality of choice for the evaluation of complex liver lesions and HCC because of its inherent ability to depict cellularity, fat, and hepatocyte composition with high soft tissue contrast. The imaging features of progressed HCC are well described. However, many HCC tumors do not demonstrate classical imaging features, posing a diagnostic dilemma to radiologists. Some of these can be attributed to variations in tumor biology and histology, which result in radiological features that differ from the typical progressed HCC. This pictorial review seeks to demonstrate the appearance of different variants of HCC on MRI imaging, in relation to their histopathologic features.

(1) Purpose: To determine the association between visceral adipose tissue (VAT) and proton density fat fraction (PDFF) with magnetic resonance imaging (MRI), and hepatic steatosis (HS), non-alcoholic steatohepatitis (NASH) and hepatic fibrosis (HF) in patients with known or suspected non-alcoholic fatty liver disease (NAFLD). (2) Methods: 135 subjects that had a liver biopsy performed within 3 months (bariatric cohort) or 1 month (NAFLD cohort) of an MRI exam formed the study group. VAT volume was quantified at L2-L3 level on opposed-phase images with signal intensity-based painting using a semi-quantitative software. Liver PDFF and pancreas PDFF were calculated on fat fraction maps. Liver volume (Lvol) and spleen volume (Svol) were also calculated using a semi-automated 3D volume tool available on PACS. A histological analysis was performed by an expert hepatopathologist blinded to imaging findings. (3) Results: The mean Lvol, Svol, liver PDFF, pancreas PDFF and VAT of the study population were 2492.2 mL, 381.6 mL, 13.2%, 12.7% and 120.6 mL, respectively. VAT showed moderate correlation with liver PDFF (r = 0.41, p < 0.001) and weak correlation with Lvol (r = 0.38, p < 0.001), Svol (r = 0.20, p = 0.025) and pancreas PDFF (rs = 0.29, p = 0.001). VAT, Lvol and liver PDFF were significantly higher in patients with HS (p < 0.001), NASH (p < 0.05) and HF (p < 0.05). VAT was also significantly higher in the presence of lobular inflammation (p = 0.019) and hepatocyte ballooning (p = 0.001). The cut-off VAT volumes for predicting HS, NASH and HF were 101.8 mL (AUC, 0.7), 111.8 mL (AUC, 0.64) and 111.6 mL (AUC, 0.66), respectively. (4) Conclusion: The MRI determined VAT can be used for predicting the presence of HS, NASH and HF in patients with known or suspected NAFLD.

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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.