LI-RADS: A Conceptual and Historical Review from Its Beginning to Its Recent Integration into AASLD Clinical Practice Guidance
Bibliographic record
Abstract
Khaled M Elsayes,1 Ania Z Kielar,2 Victoria Chernyak,3 Ali Morshid,1 Alessandro Furlan,4 William R Masch,5 Robert M Marks,6 Aya Kamaya,7 Richard KG Do,8 Yuko Kono,9 Kathryn J Fowler,9 An Tang,10 Mustafa R Bashir,11 Elizabeth M Hecht,12 Kedar Jambhekar,13 Andrej Lyshchik,14 Shuchi K Rodgers,14 Jay P Heiken,15 Marc Kohli,16 David T Fetzer,17 Stephanie R Wilson,18 Zahra Kassam,19 Mishal Mendiratta-Lala,5 Amit G Singal,17 Christopher S Lim,20 Irene Cruite,21 James Lee,22 Ryan Ash,23 Donald G Mitchell,14 Matthew DF McInnes,24 Claude B Sirlin9 1Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 2Department of Radiology, University of Toronto, ON, Canada; 3Department of Radiology, Montefiore Medical Center, Bronx, NY, USA; 4Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA; 5Department of Radiology, University of Michigan, Ann Arbor, MI, USA; 6Department of Radiology, Naval Medical Center San Diego, Uniformed Services University of the Health Sciences, Bethesda, MD, USA; 7Department of Radiology, Stanford University Medical Center, Stanford, CA, USA; 8Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 9Department of Radiology, University of California San Diego, CA, USA; 10Department of Radiology, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, QC, Canada; 11Department of Radiology, Center for Advanced Magnetic Resonance Development, and Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, NC, USA; 12Department of Radiology, Columbia University Medical Center, New York, NY, USA; 13Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 14Department of Radiology, Einstein Medical Center, Philadelphia, PA, USA; 15Department of Radiology, Mayo Clinic, Rochester, MN, USA; 16Department of Radiology, University of California San Francisco, CA, USA; 17Division of Digestive and Liver Diseases, UT Southwestern Medical Center, Dallas, TX, USA; 18Department of Radiology, University of Calgary, Calgary, AB, Canada; 19Department of Diagnostic Imaging, Schulich School of Medicine, London, ON, Canada; 20Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, ON, Canada; 21Department of Radiology, Inland Imaging, Spokane, WA, USA; 22Department of Radiology, University of Kentucky, Lexington, KY, USA; 23Department of Radiology, University of Kansas, Kansas City, KS, USA; 24Department of Radiology, University of Ottawa, ON, Canada Abstract: The Liver Imaging Reporting and Data System (LI-RADS®) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver observations in individuals at high risk for hepatocellular carcinoma (HCC). LI-RADS is supported and endorsed by the American College of Radiology (ACR). Upon its initial release in 2011, LI-RADS applied only to liver observations identified at CT or MRI. It has since been refined and expanded over multiple updates to now also address ultrasound-based surveillance, contrast-enhanced ultrasound for HCC diagnosis, and CT/MRI for assessing treatment response after locoregional therapy. The LI-RADS 2018 version was integrated into the HCC diagnosis, staging, and management practice guidance of the American Association for the Study of Liver Diseases (AASLD). This article reviews the major LI-RADS updates since its 2011 inception and provides an overview of the currently published LI-RADS algorithms. Keywords: LI-RADS, v2018, CT, MRI, CEUS, US, HCC, liver imaging, reporting, cirrhosis
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".