Will Gd-EOB-MRI Change the Diagnostic Algorithm in Hepatocellular Carcinoma?

Oncology ◽  
2010 ◽  
Vol 78 (1) ◽  
pp. 87-93 ◽  
Author(s):  
Masatoshi Kudo
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Algorithm

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

2017 ◽  
Vol 50 (2) ◽  
pp. 115-125 ◽  
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.

Surveillance and Diagnostic Algorithm for Hepatocellular Carcinoma Proposed by the Liver Cancer Study Group of Japan: 2014 Update

Oncology ◽  
2014 ◽  
Vol 87 (s1) ◽  
pp. 7-21 ◽  
Author(s):  
Masatoshi Kudo ◽  
Osamu Matsui ◽  
Namiki Izumi ◽  
Hiroko Iijima ◽  
Masumi Kadoya ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Diagnostic Algorithm ◽  
Study Group ◽  
Cancer Study ◽  
Cancer Study Group ◽  
Liver Cancer Study Group

New hallmark of hepatocellular carcinoma, early hepatocellular carcinoma and high-grade dysplastic nodules on Gd-EOB-DTPA MRI in patients with cirrhosis: a new diagnostic algorithm

Gut ◽  
2018 ◽  
Vol 67 (9) ◽  
pp. 1674-1682 ◽  
Author(s):  
Matteo Renzulli ◽  
Maurizio Biselli ◽  
Stefano Brocchi ◽  
Alessandro Granito ◽  
Francesco Vasuri ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Algorithm ◽  
High Grade ◽  
Mri Findings ◽  
Entire Study ◽  
Dysplastic Nodules ◽  
Early Hcc ◽  
Study Population ◽  
And Diffusion ◽  
Higher Sensitivity

ObjectiveMany improvements have been made in diagnosing hepatocellular carcinoma (HCC), but the radiological hallmarks of HCC have remained the same for many years. We prospectively evaluated the imaging criteria of HCC, early HCC and high-grade dysplastic nodules (HGDNs) in patients under surveillance for chronic liver disease, using gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) MRI and diffusion-weighted imaging.DesignOur study population included 420 nodules >1 cm in 228 patients. The MRI findings of each nodule were collected in all sequences/phases. The diagnosis of HCC was made according to the American Association for the Study of Liver Diseases (AASLD) criteria; all atypical nodules were diagnosed using histology.ResultsA classification and regression tree was developed using three MRI findings which were independently significant correlated variables for early HCC/HCC, and the best sequence of their application in a new diagnostic algorithm (hepatobiliary hypointensity, arterial hyperintensity and diffusion restriction) was suggested. This algorithm demonstrated, both in the entire study population and for nodules ≤2 cm, higher sensitivity (96% [95% CI 93.5% to 97.6%] and 96.6% [95% CI 93.9% to 98.5%], P<0.001, respectively) and slightly lower specificity (91.8% [95% CI 88.6% to 94.1%], P=0.063, and 92.7% [95% CI 88.9% to 95.4%], P=0.125, respectively) than those of the AASLD criteria. Our new diagnostic algorithm also showed a very high sensitivity (94.7%; 95% CI 92% to 96.6%) and specificity (99.3%; 95% CI 97.7% to 99.8%) in classifying HGDN.ConclusionOur new diagnostic algorithm demonstrated significantly higher sensitivity and comparable specificity than those of the AASLD imaging criteria for HCC in patients with cirrhosis evaluated using Gd-EOB-DTPA MRI, even for lesions ≤2 cm. Moreover, this diagnostic algorithm allowed evaluating other lesions which could arise in a cirrhotic liver, such as early HCC and HGDN.

974 DIAGNOSTIC ALGORITHM FOR HEPATOCELLULAR CARCINOMA (HCC) IN HYPOVASCULAR NODULE OF THE LIVER; A MULTICENTER NATIONWIDE STUDY

2011 ◽  
Vol 54 ◽  
pp. S389
Author(s):  
N. Izumi ◽  
K. Tsuchiya ◽  
O. Matsui ◽  
M. Sata ◽  
R. Kuromatsu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Algorithm ◽  
Nationwide Study

scholarly journals Development of a novel diagnostic algorithm to predict NASH in HCV-positive patients

2018 ◽  
Vol 33 (2) ◽  
pp. 231-236 ◽  
Author(s):  
Andrea Gallotta ◽  
Laura Paneghetti ◽  
Viera Mrázová ◽  
Adriana Bednárová ◽  
Dáša Kružlicová ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Clinical Data ◽  
Diagnostic Performance ◽  
Severe Disease ◽  
Density Lipoprotein ◽  
Diagnostic Algorithm ◽  
Immunoglobulin M ◽  
Model Assessment

Non-alcoholic steato-hepatitis (NASH) is a severe disease characterised by liver inflammation and progressive hepatic fibrosis, which may progress to cirrhosis and hepatocellular carcinoma. Clinical evidence suggests that in hepatitis C virus patients steatosis and NASH are associated with faster fibrosis progression and hepatocellular carcinoma. A safe and reliable non-invasive diagnostic method to detect NASH at its early stages is still needed to prevent progression of the disease. We prospectively enrolled 91 hepatitis C virus-positive patients with histologically proven chronic liver disease: 77 patients were included in our study; of these, 10 had NASH. For each patient, various clinical and serological variables were collected. Different algorithms combining squamous cell carcinoma antigen-immunoglobulin-M (SCCA-IgM) levels with other common clinical data were created to provide the probability of having NASH. Our analysis revealed a statistically significant correlation between the histological presence of NASH and SCCA-IgM, insulin, homeostasis model assessment, haemoglobin, high-density lipoprotein and ferritin levels, and smoke. Compared to the use of a single marker, algorithms that combined four, six or seven variables identified NASH with higher accuracy. The best diagnostic performance was obtained with the logistic regression combination, which included all seven variables correlated with NASH. The combination of SCCA-IgM with common clinical data shows promising diagnostic performance for the detection of NASH in hepatitis C virus patients.

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