Literature DB >> 28648125

Accuracy of the diagnostic evaluation of hepatocellular carcinoma with LI-RADS.

Weimin Liu1, Jie Qin1, Ruomi Guo1, Sidong Xie1, Hang Jiang1, Xiaohong Wang1, Zhuang Kang1, Jin Wang1, Hong Shan2,3,4.   

Abstract

Background There are few studies about the Liver Imaging Reporting and Data System (LI-RADS), which was developed with the purpose of standardizing the interpretation and reporting of liver imaging examinations in patients at risk for hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic accuracy of HCC diagnosis using LI-RADS. Material and Methods The computed tomography (CT), magnetic resonance imaging (MRI), and clinical data of 297 lesions in 249 patients between June 2012 and August 2013 were retrospectively analyzed. Using LI-RADS 2014, two radiologists evaluated the lesions and a LI-RADS category was retrospectively assigned to each nodule. Results The final diagnoses of 297 nodules in 249 patients consisted of 191 malignant and 106 benign lesions. Out of 44 LI-RADS category 1 lesions, none were HCCs. However, 2/25 category 2 lesions, 3/35 category 3 lesions, 16/25 category 4 lesions, 151/156 category 5 lesions, and 3/12 category LRM/OM (probable malignancy, not specific for HCC/other malignancy) lesions were HCCs. The Kappa value was 0.44 (95% confidence interval [CI] = 0.39-0.49) between two observers during LI-RADS grading. Conclusion The negative predictive value of LI-RADS category 1 was 100%. In addition, a relevant proportion of lesions categorized as category 2 or 3, or even as other malignancies, were HCCs. LI-RADS category 5 had a high specificity for HCC. LI-RADS was not able to give a differential diagnosis for the false-positive lesions of LI-RADS category 5.

Entities:  

Keywords:  Liver; Liver Imaging Reporting and Data System; chronic liver disease; hepatocellular carcinoma

Mesh:

Year:  2017        PMID: 28648125     DOI: 10.1177/0284185117716700

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  13 in total

1.  Longitudinal evolution of CT and MRI LI-RADS v2014 category 1, 2, 3, and 4 observations.

Authors:  Cheng William Hong; Charlie C Park; Adrija Mamidipalli; Jonathan C Hooker; Soudabeh Fazeli Dehkordy; Saya Igarashi; Mohanad Alhumayed; Yuko Kono; Rohit Loomba; Tanya Wolfson; Anthony Gamst; Paul Murphy; Claude B Sirlin
Journal:  Eur Radiol       Date:  2019-02-26       Impact factor: 5.315

2.  Evaluation of LI-RADS 3 category by magnetic resonance in US-detected nodules ≤ 2 cm in cirrhotic patients.

Authors:  Anna Darnell; Jordi Rimola; Ernest Belmonte; Enric Ripoll; Ángeles Garcia-Criado; Carla Caparroz; Álvaro Díaz-González; Ramón Vilana; María Reig; Carmen Ayuso; Jordi Bruix; Alejandro Forner
Journal:  Eur Radiol       Date:  2021-01-06       Impact factor: 5.315

Review 3.  Joint Consensus Statement of the Indian National Association for Study of the Liver and Indian Radiological and Imaging Association for the Diagnosis and Imaging of Hepatocellular Carcinoma Incorporating Liver Imaging Reporting and Data System.

Authors:  Sonal Krishan; Radha K Dhiman; Navin Kalra; Raju Sharma; Sanjay S Baijal; Anil Arora; Ajay Gulati; Anu Eapan; Ashish Verma; Shyam Keshava; Amar Mukund; S Deva; Ravi Chaudhary; Karthick Ganesan; Sunil Taneja; Ujjwal Gorsi; Shivanand Gamanagatti; Kumble S Madhusudan; Pankaj Puri; Shallini Govil; Manav Wadhavan; Sanjiv Saigal; Ashish Kumar; Shallini Thapar; Ajay Duseja; Neeraj Saraf; Anubhav Khandelwal; Sumit Mukhopadyay; Ajay Gulati; Nitin Shetty; Nipun Verma
Journal:  J Clin Exp Hepatol       Date:  2019-08-06

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

Authors:  Charlie A Hamm; Clinton J Wang; Lynn J Savic; Marc Ferrante; Isabel Schobert; Todd Schlachter; MingDe Lin; James S Duncan; Jeffrey C Weinreb; Julius Chapiro; Brian Letzen
Journal:  Eur Radiol       Date:  2019-04-23       Impact factor: 5.315

Review 5.  Overdiagnosis of hepatocellular carcinoma: Prevented by guidelines?

Authors:  Nicole E Rich; Amit G Singal
Journal:  Hepatology       Date:  2022-01-18       Impact factor: 17.425

6.  Tumor size-based validation of contrast-enhanced ultrasound liver imaging reporting and data system (CEUS LI-RADS) 2017 for hepatocellular carcinoma characterizing.

Authors:  Jia-Min Pan; Wei Chen; Yan-Ling Zheng; Mei-Qing Cheng; Dan Zeng; Hui Huang; Yang Huang; Xiao-Yan Xie; Ming-De Lu; Ming Kuang; Hang-Tong Hu; Li-Da Chen; Wei Wang
Journal:  Br J Radiol       Date:  2021-10-01       Impact factor: 3.629

7.  Added value of enhanced CT on LR-3 and LR-4 observation of Gd-EOB-DTPA MRI for the diagnosis of HCC: are CT and MR washout features interchangeable?

Authors:  Kyungjae Lim; Heejin Kwon; Jinhan Cho; Dongwon Kim; Eunju Kang; Sanghyeon Kim
Journal:  Br J Radiol       Date:  2022-01-05       Impact factor: 3.629

8.  Contributions of Magnetic Resonance Imaging to Gastroenterological Practice: MRIs for GIs.

Authors:  Christopher G Roth; Dina Halegoua-De Marzio; Flavius F Guglielmo
Journal:  Dig Dis Sci       Date:  2018-05       Impact factor: 3.199

Review 9.  Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients.

Authors:  Victoria Chernyak; Kathryn J Fowler; Aya Kamaya; Ania Z Kielar; Khaled M Elsayes; Mustafa R Bashir; Yuko Kono; Richard K Do; Donald G Mitchell; Amit G Singal; An Tang; Claude B Sirlin
Journal:  Radiology       Date:  2018-09-25       Impact factor: 11.105

10.  Diagnostic performance of the LR-M criteria and spectrum of LI-RADS imaging features among primary hepatic carcinomas.

Authors:  Seung-Seob Kim; Sunyoung Lee; Jin-Young Choi; Joon Seok Lim; Mi-Suk Park; Myeong-Jin Kim
Journal:  Abdom Radiol (NY)       Date:  2020-11
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