Liver Imaging Reporting and Data System: an expert consensus statement
Bibliographic record
Abstract
Khaled M Elsayes,1 Ania Z Kielar,2 Michelle M Agrons,3 Janio Szklaruk,1 An Tang,4 Mustafa R Bashir,5 Donald G Mitchell,6 Richard K Do,7 Kathryn J Fowler,8 Victoria Chernyak,9 Claude B Sirlin10 1Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 2Department of Diagnostic Radiology, University of Ottawa, Ottawa, ON, Canada; 3Department of Diagnostic Radiology, Baylor College of Medicine, Houston, TX, USA; 4Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montreal, QC, Canada; 5Department of Diagnostic Radiology, Duke University School of Medicine, Durham, NC, 6Department of Diagnostic Radiology, Thomas Jefferson University, Philadelphia, PA, 7Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 8Mallinckrodt Institute of Radiology, Washington University in Saint Louis, Saint Louis, MO, 9Department of Radiology Albert Einstein College of Medicine, Bronx, New York, NY, 10Department of Diagnostic Radiology, University of California, San Diego, CA, USA Abstract: The increasing incidence and high morbidity and mortality of hepatocellular carcinoma (HCC) have inspired the creation of the Liver Imaging Reporting and Data System (LI-RADS). LI-RADS aims to reduce variability in exam interpretation, improve communication, facilitate clinical therapeutic decisions, reduce omission of pertinent information, and facilitate the monitoring of outcomes. LI-RADS is a dynamic process, which is updated frequently. In this article, we describe the LI-RADS 2014 version (v2014), which marks the second update since the initial version in 2011. Keywords: hepatocellular carcinoma, imaging, reporting, cirrhosis, hyperenhancement washout
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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 | 0.223 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.017 | 0.008 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".