LI-RADS Version 2018 Ancillary Features at MRI
Bibliographic record
Abstract
The Liver Imaging Reporting and Data System (LI-RADS) standardizes performance of liver imaging in patients at risk for hepatocellular carcinoma (HCC) as well as interpretation and reporting of the results. Developed by experts in liver imaging and supported by the American College of Radiology, LI-RADS assigns to observations categories that reflect the relative probability of benignity, HCC, or other malignancy. While category assignment is based mainly on major imaging features, ancillary features may be applied to improve detection and characterization, increase confidence, or adjust LI-RADS categories. Ancillary features are classified as favoring malignancy in general, HCC in particular, or benignity. Those favoring malignancy in general or HCC in particular may be used to upgrade by a maximum of one category up to LR-4; those favoring benignity may be used to downgrade by a maximum of one category. If there are conflicting ancillary features (ie, one or more favoring malignancy and one or more favoring benignity), the category should not be adjusted. Ancillary features may be seen at diagnostic CT, MRI performed with extracellular agents, or MRI performed with hepatobiliary agents, with the exception of one ancillary feature assessed at US. This article focuses on LI-RADS version 2018 ancillary features seen at MRI. Specific topics include rules for ancillary feature application; definitions, rationale, and illustrations with clinical MRI examples; summary of evidence and diagnostic performance; pitfalls; and future directions. ©RSNA, 2018
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.067 |
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".