Literature DB >> 29091751

Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study.

Kathryn J Fowler1, An Tang1, Cynthia Santillan1, Mythreyi Bhargavan-Chatfield1, Jay Heiken1, Reena C Jha1, Jeffrey Weinreb1, Hero Hussain1, Donald G Mitchell1, Mustafa R Bashir1, Eduardo A C Costa1, Guilherme M Cunha1, Laura Coombs1, Tanya Wolfson1, Anthony C Gamst1, Giuseppe Brancatelli1, Benjamin Yeh1, Claude B Sirlin1.   

Abstract

Purpose To determine in a large multicenter multireader setting the interreader reliability of Liver Imaging Reporting and Data System (LI-RADS) version 2014 categories, the major imaging features seen with computed tomography (CT) and magnetic resonance (MR) imaging, and the potential effect of reader demographics on agreement with a preselected nonconsecutive image set. Materials and Methods Institutional review board approval was obtained, and patient consent was waived for this retrospective study. Ten image sets, comprising 38-40 unique studies (equal number of CT and MR imaging studies, uniformly distributed LI-RADS categories), were randomly allocated to readers. Images were acquired in unenhanced and standard contrast material-enhanced phases, with observation diameter and growth data provided. Readers completed a demographic survey, assigned LI-RADS version 2014 categories, and assessed major features. Intraclass correlation coefficient (ICC) assessed with mixed-model regression analyses was the metric for interreader reliability of assigning categories and major features. Results A total of 113 readers evaluated 380 image sets. ICC of final LI-RADS category assignment was 0.67 (95% confidence interval [CI]: 0.61, 0.71) for CT and 0.73 (95% CI: 0.68, 0.77) for MR imaging. ICC was 0.87 (95% CI: 0.84, 0.90) for arterial phase hyperenhancement, 0.85 (95% CI: 0.81, 0.88) for washout appearance, and 0.84 (95% CI: 0.80, 0.87) for capsule appearance. ICC was not significantly affected by liver expertise, LI-RADS familiarity, or years of postresidency practice (ICC range, 0.69-0.70; ICC difference, 0.003-0.01 [95% CI: -0.003 to -0.01, 0.004-0.02]. ICC was borderline higher for private practice readers than for academic readers (ICC difference, 0.009; 95% CI: 0.000, 0.021). Conclusion ICC is good for final LI-RADS categorization and high for major feature characterization, with minimal reader demographic effect. Of note, our results using selected image sets from nonconsecutive examinations are not necessarily comparable with those of prior studies that used consecutive examination series. © RSNA, 2017.

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Year:  2017        PMID: 29091751     DOI: 10.1148/radiol.2017170376

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  25 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.  Combined hepatocellular cholangiocarcinoma: LI-RADS v2017 categorisation for differential diagnosis and prognostication on gadoxetic acid-enhanced MR imaging.

Authors:  Sun Kyung Jeon; Ijin Joo; Dong Ho Lee; Sang Min Lee; Hyo-Jin Kang; Kyoung-Bun Lee; Jeong Min Lee
Journal:  Eur Radiol       Date:  2018-06-28       Impact factor: 5.315

Review 3.  Pitfalls and problems to be solved in the diagnostic CT/MRI Liver Imaging Reporting and Data System (LI-RADS).

Authors:  Yeun-Yoon Kim; Jin-Young Choi; Claude B Sirlin; Chansik An; Myeong-Jin Kim
Journal:  Eur Radiol       Date:  2018-08-16       Impact factor: 5.315

4.  Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study.

Authors:  Rikiya Yamashita; Amber Mittendorf; Zhe Zhu; Kathryn J Fowler; Cynthia S Santillan; Claude B Sirlin; Mustafa R Bashir; Richard K G Do
Journal:  Abdom Radiol (NY)       Date:  2020-01

5.  Hepatocellular carcinoma (HCC) versus non-HCC: accuracy and reliability of Liver Imaging Reporting and Data System v2018.

Authors:  Daniel R Ludwig; Tyler J Fraum; Roberto Cannella; David H Ballard; Richard Tsai; Muhammad Naeem; Maverick LeBlanc; Amber Salter; Allan Tsung; Anup S Shetty; Amir A Borhani; Alessandro Furlan; Kathryn J Fowler
Journal:  Abdom Radiol (NY)       Date:  2019-06

6.  A radiogenomic analysis of hepatocellular carcinoma: association between fractional allelic imbalance rate index and the liver imaging reporting and data system (LI-RADS) categories and features.

Authors:  Alessandro Furlan; Omar Almusa; Robinson K Yu; Hersh Sagreiya; Amir A Borhani; Kyongtae T Bae; J Wallis Marsh
Journal:  Br J Radiol       Date:  2018-04-04       Impact factor: 3.039

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

Authors:  Clinton J Wang; Charlie A Hamm; Lynn J Savic; Marc Ferrante; Isabel Schobert; Todd Schlachter; MingDe Lin; Jeffrey C Weinreb; James S Duncan; Julius Chapiro; Brian Letzen
Journal:  Eur Radiol       Date:  2019-05-15       Impact factor: 5.315

8.  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 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.  Validation of the Liver Imaging Reporting and Data System Treatment Response Criteria After Thermal Ablation for Hepatocellular Carcinoma.

Authors:  Katherine S Cools; Andrew M Moon; Lauren M B Burke; Katrina A McGinty; Paula D Strassle; David A Gerber
Journal:  Liver Transpl       Date:  2019-12-20       Impact factor: 5.799

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