Literature DB >> 29318354

LI-RADS: a glimpse into the future.

Claude B Sirlin1, Ania Z Kielar2,3, An Tang4,5, Mustafa R Bashir6,7.   

Abstract

This article provides a glimpse into the future of the Liver Imaging Reporting and Data System (LI-RADS), discussing the immediate and long-term plans for its continuing improvement and expansion. To complement the Core and Essentials components of the latest version of LI-RADS, a comprehensive manual will be released soon, and it will include technical recommendations, management guidance, as well as reporting instructions and templates. In this article, we briefly review the process by which LI-RADS has been developed until now, a process guided by a variable combination of data, expert opinion, and desire for congruency with other diagnostic systems in North America. We then look forward, envisioning that forthcoming updates to LI-RADS will occur regularly every 3 to 5 years, driven by emerging high-quality scientific evidence. We highlight some of the key knowledge and technology gaps that will need to be addressed to enable the needed refinements. We also anticipate future expansions in scope to meet currently unaddressed clinical needs. Finally, we articulate a vision for eventual unification of imaging system for HCC screening and surveillance, diagnosis and staging, and treatment response assessment.

Keywords:  Diagnosis; Diagnostic imaging; Hepatocellular carcinoma; Practice guidelines; Screening and surveillance

Mesh:

Substances:

Year:  2018        PMID: 29318354     DOI: 10.1007/s00261-017-1448-1

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  5 in total

1.  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

2.  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 3.  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

Review 4.  Epidemiology and Management of Hepatocellular Carcinoma.

Authors:  Laura Kulik; Hashem B El-Serag
Journal:  Gastroenterology       Date:  2018-10-24       Impact factor: 22.682

5.  Atypical magnetic resonance imaging features and differential diagnosis of hepatocellular carcinoma.

Authors:  Shuang-Yu Wang; Lei Yin; Chen Wang; Ming-Ping Ma
Journal:  J Int Med Res       Date:  2020-10       Impact factor: 1.671

  5 in total

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