LI‐RADS 2017: An update
Bibliographic record
Abstract
The computed tomography / magnetic resonance imaging (CT/MRI) Liver Imaging Reporting & Data System (LI-RADS) is a standardized system for diagnostic imaging terminology, technique, interpretation, and reporting in patients with or at risk for developing hepatocellular carcinoma (HCC). Using diagnostic algorithms and tables, the system assigns to liver observations category codes reflecting the relative probability of HCC or other malignancies. This review article provides an overview of the 2017 version of CT/MRI LI-RADS with a focus on MRI. The main LI-RADS categories and their application will be described. Changes and updates introduced in this version of LI-RADS will be highlighted, including modifications to the diagnostic algorithm and to the optional application of ancillary features. Comparisons to other major diagnostic systems for HCC will be made, emphasizing key similarities, differences, strengths, and limitations. In addition, this review presents the new Treatment Response algorithm, while introducing the concepts of MRI nonviability and viability. Finally, planned future directions for LI-RADS will be outlined. LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1459-1474.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".