Imaging features of histological subtypes of hepatocellular carcinoma: implication for LI-RADS

Hepatocellular carcinoma (HCC) is a heterogeneous disease with diverse characteristics and outcomes. Here, we aim to develop a histological classification for HCC by integrating computational imaging features of the tumor and its microenvironment. We first trained a multitask deep-learning neural network for automated single-cell segmentation and classification on hematoxylin- and eosin-stained tissue sections. After confirming the accuracy in a testing set, we applied the model to whole-slide images of 304 tumors in the Cancer Genome Atlas. Given the single-cell map, we calculated 246 quantitative image features to characterize individual nuclei as well as spatial relations between tumor cells and infiltrating lymphocytes. Unsupervised consensus clustering revealed three reproducible histological subtypes, which exhibit distinct nuclear features as well as spatial distribution and relation between tumor cells and lymphocytes. These histological subtypes were associated with somatic genomic alterations (i.e., aneuploidy) and specific molecular pathways, including cell cycle progression and oxidative phosphorylation. Importantly, these histological subtypes complement established molecular classification and demonstrate independent prognostic value beyond conventional clinicopathologic factors. Our study represents a step forward in quantifying the spatial distribution and complex interaction between tumor and immune microenvironment. The clinical relevance of the imaging subtypes for predicting prognosis and therapy response warrants further validation.

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Added-value of ancillary imaging features for differentiating hepatocellular carcinoma from intrahepatic mass-forming cholangiocarcinoma on Gd-BOPTA-enhanced MRI in LI-RADS M

Abdominal Radiology ◽

10.1007/s00261-021-03380-6 ◽

2021 ◽

Author(s):

Wanjing Zheng ◽

Hongjie Huang ◽

Dejun She ◽

Meilian Xiong ◽

Xiaodan Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Added Value ◽

Imaging Features ◽

Enhanced Mri

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Hepatocellular carcinoma with bile duct tumor thrombi: Correlation of magnetic resonance imaging features to histopathologic manifestations

European Journal of Radiology ◽

10.1016/j.ejrad.2009.05.020 ◽

2010 ◽

Vol 76 (1) ◽

pp. 103-109 ◽

Cited By ~ 12

Author(s):

Qingyu Liu ◽

Jianyu Chen ◽

Haigang Li ◽

Biling Liang ◽

Lei Zhang ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Hepatocellular Carcinoma ◽

Bile Duct ◽

Magnetic Resonance ◽

Imaging Features ◽

Resonance Imaging ◽

Bile Duct Tumor ◽

Tumor Thrombi

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An update on the histological subtypes of hepatocellular carcinoma

Hepatoma Research ◽

10.20517/2394-5079.2019.021 ◽

2019 ◽

Vol 2019 ◽

Cited By ~ 1

Author(s):

Regina Cheuk-Lam Lo

Keyword(s):

Hepatocellular Carcinoma ◽

Histological Subtypes

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Imaging Features of Fibrolamellar Hepatocellular Carcinoma

American Journal of Roentgenology ◽

10.2214/ajr.13.11117 ◽

2014 ◽

Vol 202 (3) ◽

pp. 544-552 ◽

Cited By ~ 43

Author(s):

Dhakshinamoorthy Ganeshan ◽

Janio Szklaruk ◽

Vikas Kundra ◽

Ahmed Kaseb ◽

Asif Rashid ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Imaging Features ◽

Fibrolamellar Hepatocellular Carcinoma

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Magnetic resonance imaging of the cirrhotic liver: diagnosis of hepatocellular carcinoma and evaluation of response to treatment – Part 2

Radiologia Brasileira ◽

10.1590/0100-3984.2015.0140 ◽

2017 ◽

Vol 50 (2) ◽

pp. 115-125 ◽

Cited By ~ 8

Author(s):

Miguel Ramalho ◽

António P. Matos ◽

Mamdoh AlObaidy ◽

Fernanda Velloni ◽

Ersan Altun ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Hepatocellular Carcinoma ◽

Magnetic Resonance ◽

Diagnostic Algorithm ◽

Response To Treatment ◽

Locoregional Therapy ◽

Liver Imaging ◽

Imaging Features ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Mri

Abstract In the second part of this review, we will describe the ancillary imaging features of hepatocellular carcinoma (HCC) that can be seen on standard magnetic resonance imaging (MRI) protocol, and on novel and emerging protocols such as diffusion weighted imaging and utilization of hepatocyte-specific/hepatobiliary contrast agent. We will also describe the morphologic sub-types of HCC, and give a simplified non-invasive diagnostic algorithm for HCC, followed by a brief description of the liver imaging reporting and data system (LI-RADS), and MRI assessment of tumor response following locoregional therapy.

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Diagnostic performance of imaging features in the HBP of gadoxetate disodium-enhanced MRI for microvascular invasion in hepatocellular carcinoma: a meta-analysis

Acta Radiologica ◽

10.1177/02841851211038806 ◽

2021 ◽

pp. 028418512110388

Author(s):

Yuhui Deng ◽

Dawei Yang ◽

Hui Xu ◽

Ahong Ren ◽

Zhenghan Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Performance ◽

Meta Analysis ◽

Microvascular Invasion ◽

Index Test ◽

Imaging Features ◽

Gadoxetate Disodium ◽

Reference Test ◽

Tumor Margin ◽

Meta Regression

Background Microvascular invasion (MVI) is a major risk factor for early recurrence in patients with hepatocellular carcinoma (HCC). Preoperative accurate evaluation of the presence of MVI could enormously benefit its treatment and prognosis. Purpose To evaluate and compare the diagnostic performance of two imaging features (non-smooth tumor margin and peritumor hypointensity) in the hepatobiliary phase (HBP) to preoperatively diagnose the presence of MVI in HCC. Material and Methods Original articles were collected from Medline/PubMed, Web of Science, EMBASE, and the Cochrane Library up to 17 January 2021 linked to gadoxetate disodium–enhanced magnetic resonance imaging (MRI) on 1.5 or 3.0 T. The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUC) were calculated and meta-regression analyses were performed. Results A total of 14 original articles involving 2193 HCCs were included. The pooled sensitivity and specificity of non-smooth tumor margin and peritumor hypointensity were 73% and 61%, and 43% and 90%, respectively, for the diagnosis of MVI in HCC. The summary AUC of non-smooth tumor margin (0.74) was comparable to that of peritumor hypointensity (0.76) ( z = 0.693, P = 0.488). The meta-regression analysis identified four covariates as possible sources of heterogeneity: average size; time interval between index test and reference test; blindness to index test during reference test; and risk of bias score. Conclusion This meta-analysis showed moderate and comparable accuracy for predicting MVI in HCC using either non-smooth tumor margin or peritumor hypointensity in HBP. Four discovered covariates accounted for the heterogeneity.

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Hepatocellular carcinoma in the non-cirrhotic liver

Clinical Hemorheology and Microcirculation ◽

10.3233/ch-211309 ◽

2021 ◽

pp. 1-14

Author(s):

Dong Yi ◽

Wang Wen-Ping ◽

Won Jae Lee ◽

Maria Franca Meloni ◽

Dirk-Andre Clevert ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cirrhosis ◽

Risk Factor ◽

High Risk ◽

Current Knowledge ◽

Cirrhotic Liver ◽

Imaging Features ◽

Advanced Stage ◽

High Risk Factor ◽

Cirrhotic Patients

Liver cirrhosis is an established high-risk factor for HCC and the majority of patients diagnosed with HCC have cirrhosis. However, HCC also arises in non-cirrhotic livers in approximately 20 %of all cases. HCC in non-cirrhotic patients is often clinically silent and surveillance is usually not recommended. HCC is often diagnosed at an advanced stage in these patients. Current information about HCC in patients with non-cirrhotic liver is limited. Here we review the current knowledge on epidemiology, clinical features and imaging features of those patiens.

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