LI-RADS Version 2017 versus Version 2018: Diagnosis of Hepatocellular Carcinoma on Gadoxetate Disodium–enhanced MRI
Bibliographic record
Abstract
Background Few studies have reported on the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) LR-5 or LR-5 V in the diagnosis of hepatocellular carcinoma (HCC) using MRI with gadoxetate disodium. Purpose To determine the diagnostic performance of LI-RADS version 2018 (hereafter, v2018) on gadoxetate disodium–enhanced MRI in comparison with LI-RADS version 2017 (hereafter, v2017) for the diagnosis of HCC in patients with cirrhosis or chronic hepatitis B viral infection or at high risk for HCC. Materials and Methods This retrospective study between January 2013 and October 2015 evaluated consecutive patients at high risk for HCC who had at least one observation of 10 mm or greater on gadoxetate disodium–enhanced MRI and no history of previous treatment for hepatic lesions. MRI features were reviewed by three radiologists. Observations were categorized according to LI-RADS v2018 and LI-RADS v2017. Per-observation sensitivity and specificity of LR-5 using LI-RADS v2017 and v2018 were compared using generalized estimating equation models. Results A total of 422 observations, including 234 HCCs confirmed by results of pathologic examination in 387 patients (305 men and 82 women; mean age ± standard deviation, 59 years ± 10), were included. In all observations, LI-RADS v2018 provided higher sensitivity than LI-RADS v2017 (81% [189 of 234] vs 68% [160 of 234], respectively; P < .001). In small observations (10–19 mm), LI-RADS v2018 yielded much higher sensitivity than LI-RADS v2017 (76% [34 of 45] vs 11% [five of 45], respectively; P < .001) with relatively little impairment of specificity (94% [121 of 128] vs 99% [127 of 128], respectively; P = .013). Conclusion Updated LR-5 criteria of Liver Imaging Reporting and Data System (LI-RADS) version 2018 on gadoxetate disodium–enhanced MRI can improve sensitivity in the diagnosis of small hepatocellular carcinomas (10–19 mm) with only slight impairment in specificity compared with the criteria of LI-RADS version 2017. © RSNA, 2019 Online supplemental material is available for this article.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".