Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients

V Chernyak, KJ Fowler, A Kamaya, AZ Kielar… - Radiology, 2018 - pubs.rsna.org
The Liver Imaging Reporting and Data System (LI-RADS) is composed of four individual
algorithms intended to standardize the lexicon, as well as reporting and care, in patients with …

Evidence supporting LI-RADS major features for CT-and MR imaging–based diagnosis of hepatocellular carcinoma: a systematic review

A Tang, MR Bashir, MT Corwin, I Cruite, CF Dietrich… - Radiology, 2018 - pubs.rsna.org
The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation,
reporting, and data collection for imaging examinations in patients at risk for hepatocellular …

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

CA Hamm, CJ Wang, LJ Savic, M Ferrante… - European …, 2019 - Springer
Objectives To develop and validate a proof-of-concept convolutional neural network (CNN)–
based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic …

Interobserver reproducibility of the PI-RADS version 2 lexicon: a multicenter study of six experienced prostate radiologists

AB Rosenkrantz, LA Ginocchio, D Cornfeld… - Radiology, 2016 - pubs.rsna.org
Purpose To determine the interobserver reproducibility of the Prostate Imaging Reporting
and Data System (PI-RADS) version 2 lexicon. Materials and Methods This retrospective …

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

CJ Wang, CA Hamm, LJ Savic, M Ferrante… - European …, 2019 - Springer
Objectives To develop a proof-of-concept “interpretable” deep learning prototype that
justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods A …

EASL and AASLD recommendations for the diagnosis of HCC to the test of daily practice

C Aubé, F Oberti, J Lonjon, G Pageaux… - Liver …, 2017 - Wiley Online Library
Aims To evaluate the diagnostic performance of CT, MRI and CEUS alone and in
combination, for the diagnosis of HCC between 10 and 30 mm, in a large population of …

Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the …

PM Oestmann, CJ Wang, LJ Savic, CA Hamm… - European …, 2021 - Springer
Objectives To train a deep learning model to differentiate between pathologically proven
hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical …

Differentiation of hepatocellular carcinoma from other hepatic malignancies in patients at risk: diagnostic performance of the liver imaging reporting and data system …

TJ Fraum, R Tsai, E Rohe, DR Ludwig, A Salter… - Radiology, 2018 - pubs.rsna.org
Purpose To evaluate the diagnostic performance and interrater reliability of the Liver
Imaging Reporting and Data System (LI-RADS) version 2014 in differentiating …

Liver imaging reporting and data system with MR imaging: evaluation in nodules 20 mm or smaller detected in cirrhosis at screening US

A Darnell, A Forner, J Rimola, M Reig, Á García-Criado… - Radiology, 2015 - pubs.rsna.org
Purpose To evaluate the diagnostic accuracy of the Liver Imaging Reporting and Data
System (LI-RADS) with magnetic resonance (MR) imaging for hepatic nodules 20 mm or …

Comparison of the accuracy of AASLD and LI-RADS criteria for the non-invasive diagnosis of HCC smaller than 3 cm

M Ronot, O Fouque, M Esvan, J Lebigot, C Aubé… - Journal of …, 2018 - Elsevier
Background & Aims Non-invasive imaging is crucial for the early diagnosis and successful
treatment of hepatocellular carcinoma (HCC). Terminology and criteria for interpreting and …