Literature DB >> 27174029

Reliability, Validity, and Reader Acceptance of LI-RADS-An In-depth Analysis.

Borna K Barth1, Olivio F Donati1, Michael A Fischer1, Erika J Ulbrich1, Christoph A Karlo1, Anton Becker1, Burkhard Seifert2, Caecilia S Reiner3.   

Abstract

RATIONALE AND
OBJECTIVES: This study aimed to analyze interreader agreement and diagnostic accuracy of Liver Imaging Reporting and Data System (LI-RADS) in comparison to a nonstandardized 5-point scale and to assess reader acceptance of LI-RADS for clinical routine.
MATERIALS AND METHODS: Eighty-four consecutive patients at risk for hepatocellular carcinoma who underwent liver magnetic resonance imaging were included in this Health Insurance Portability and Accountability Act-compliant retrospective study. Four readers rated the likelihood of hepatocellular carcinoma for 104 liver observations using LI-RADS criteria and a 5-point Likert scale (LIKERT) based on subjective impression in two separate reading sessions. Interreader agreement was assessed using kappa statistics (κ). Diagnostic accuracy was assessed with receiver operating characteristic analysis. Reader acceptance was evaluated with a questionnaire. A sub-analysis of LI-RADS's major features (arterial phase hyper-enhancement, washout, capsule appearance, and threshold growth) and scores for lesions </>1.5 cm was performed.
RESULTS: LI-RADS showed similar overall interreader agreement compared to LIKERT (κ, 0.44 [95%CI: 0.37, 0.52] and 0.35 [95%CI: 0.27, 0.43]) with a tendency toward higher interreader agreement for LI-RADS. Interreader agreement (κ) was 0.51 (95%CI: 0.38, 0.65) for arterial phase hyper-enhancement, 0.52 (95%CI: 0.39, 0.65) for washout, 0.37 (95%CI: 0.23, 0.52) for capsule appearance, and 0.50 (95%CI: 0.38, 0.61) for threshold growth. Overall interreader agreement for LI-RADS categories was similar between observations <1.5 cm and observations >1.5 cm. Overall diagnostic accuracy for LIKERT and LI-RADS was comparable (area under the receiver operating characteristic curve, 0.86 and 0.87). Readers fully agreed with the statement "A short version of LI-RADS would facilitate the use in clinical routine" (median, 5.0; interquartile range, 2.25).
CONCLUSIONS: LI-RADS showed similar interreader agreement and diagnostic accuracy compared to nonstandardized reporting. However, further reduction of complexity and refinement of imaging features may be needed.
Copyright © 2016. Published by Elsevier Inc.

Entities:  

Keywords:  Liver; cirrhosis; hepatocellular carcinoma; magnetic resonance imaging; neoplasm

Mesh:

Substances:

Year:  2016        PMID: 27174029     DOI: 10.1016/j.acra.2016.03.014

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  16 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.  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

3.  Interobserver and intermodality agreement of standardized algorithms for non-invasive diagnosis of hepatocellular carcinoma in high-risk patients: CEUS-LI-RADS versus MRI-LI-RADS.

Authors:  Barbara Schellhaas; Matthias Hammon; Deike Strobel; Lukas Pfeifer; Christian Kielisch; Ruediger S Goertz; Alexander Cavallaro; Rolf Janka; Markus F Neurath; Michael Uder; Hannes Seuss
Journal:  Eur Radiol       Date:  2018-04-19       Impact factor: 5.315

4.  Characterization of liver nodules in patients with chronic liver disease by MRI: performance of the Liver Imaging Reporting and Data System (LI-RADS v.2018) scale and its comparison with the Likert scale.

Authors:  Andrea Esposito; Valentina Buscarino; Dario Raciti; Elena Casiraghi; Matteo Manini; Pietro Biondetti; Laura Forzenigo
Journal:  Radiol Med       Date:  2019-10-05       Impact factor: 3.469

5.  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

6.  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 7.  Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.

Authors:  An Tang; Mustafa R Bashir; Michael T Corwin; Irene Cruite; Christoph F Dietrich; Richard K G Do; Eric C Ehman; Kathryn J Fowler; Hero K Hussain; Reena C Jha; Adib R Karam; Adrija Mamidipalli; Robert M Marks; Donald G Mitchell; Tara A Morgan; Michael A Ohliger; Amol Shah; Kim-Nhien Vu; Claude B Sirlin
Journal:  Radiology       Date:  2017-11-21       Impact factor: 11.105

Review 8.  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

9.  Imaging features of hepatocellular carcinoma compared to intrahepatic cholangiocarcinoma and combined tumor on MRI using liver imaging and data system (LI-RADS) version 2014.

Authors:  Natally Horvat; Ines Nikolovski; Niamh Long; Scott Gerst; Jian Zheng; Linda Ma Pak; Amber Simpson; Junting Zheng; Marinela Capanu; William R Jarnagin; Lorenzo Mannelli; Richard Kinh Gian Do
Journal:  Abdom Radiol (NY)       Date:  2018-01

10.  Imaging Findings Within the First 12 Months of Hepatocellular Carcinoma Treated With Stereotactic Body Radiation Therapy.

Authors:  Mishal Mendiratta-Lala; Everett Gu; Dawn Owen; Kyle C Cuneo; Latifa Bazzi; Theodore S Lawrence; Hero K Hussain; Matthew S Davenport
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-08-24       Impact factor: 7.038

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