Literature DB >> 25371354

Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications.

Mustafa R Bashir1, Rong Huang1, Nicholas Mayes1, Daniele Marin1, Carl L Berg2, Rendon C Nelson1, Tracy A Jaffe1.   

Abstract

PURPOSE: To determine the rate of agreement between the Organ Procurement and Transplant Network (OPTN) and Liver Imaging Reporting and Data System (LI-RADS) classifications for hypervascular liver nodules at least 1 cm in diameter, and for patient eligibility for hepatocellular/MELD (Model for Endstage Liver Disease) exception points.
MATERIALS AND METHODS: This retrospective study was approved by our Institutional Review Board and was compliant with the Health Insurance Portability and Accountability Act. The requirement for informed consent was waived. This study included 200 hypervascular hepatocellular nodules at least 1 cm in diameter on computed tomography (CT) or magnetic resonance imaging (MRI) examinations in 105 patients with chronic liver disease. Three radiologists blinded to clinical data independently evaluated nodule characteristics, including washout, capsule, size, and size on prior examination. Based on those characteristics, nodules were automatically classified as definite hepatocellular carcinoma (HCC) or not definite HCC using both the OPTN and LI-RADS classifications. Using these classifications and the Milan criteria, each examination was determined to be "below transplant criteria," "within transplant criteria," or "beyond transplant criteria." Agreement was assessed between readers and classification systems, using Fleiss' kappa, intraclass correlation coefficients (ICCs), and simple proportions.
RESULTS: Interreader agreement was moderate for nodule features (κ = 0.59-0.69) and nodule classification (0.66-0.69). The two systems were in nearly complete agreement on nodule category assignment (98.7% [592/600]) and patient eligibility for transplant exemption priority (99.4% [313/315]). A few discrepancies occurred for the nodule feature of growth (1.3% [8/600]) and for nodule category assignment (1.3% [8/600]).
CONCLUSION: Agreement between the OPTN and LI-RADS classifications is very strong for categorization of hypervascular liver nodules at least 1 cm in diameter, and for patient eligibility for hepatocellular/MELD exception points. Interreader variability is much higher than intersystem variability.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  LI-RADS; MELD; OPTN; hepatocellular carcinoma

Mesh:

Year:  2014        PMID: 25371354     DOI: 10.1002/jmri.24793

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  13 in total

1.  Measurement of spleen fat on MRI-proton density fat fraction arises from reconstruction of noise.

Authors:  Cheng William Hong; Gavin Hamilton; Catherine Hooker; Charlie C Park; Calvin Andrew Tran; Walter C Henderson; Jonathan C Hooker; Soudabeh Fazeli Dehkordy; Jeffrey B Schwimmer; Scott B Reeder; Claude B Sirlin
Journal:  Abdom Radiol (NY)       Date:  2019-10

Review 2.  LI-RADS and transplantation: challenges and controversies.

Authors:  Guilherme M Cunha; Dorathy E Tamayo-Murillo; Kathryn J Fowler
Journal:  Abdom Radiol (NY)       Date:  2021-01

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.  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

5.  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

6.  LI-RADS v2017 categorisation of HCC using CT: Does moderate to severe fatty liver affect accuracy?

Authors:  Seung Soo Kim; Jeong Ah Hwang; Hyeong Cheol Shin; Seo-Youn Choi; Tae Wook Kang; Sung Shick Jou; Woong Hee Lee; Suyeon Park; Nam Hun Heo
Journal:  Eur Radiol       Date:  2018-08-02       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.  LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy.

Authors:  Erin L Shropshire; Mohammad Chaudhry; Chad M Miller; Brian C Allen; Erol Bozdogan; Diana M Cardona; Lindsay Y King; Gemini L Janas; Richard K Do; Charles Y Kim; James Ronald; Mustafa R Bashir
Journal:  Radiology       Date:  2019-04-30       Impact factor: 29.146

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.