Long‐term evolution of LI‐RADS observations in HCV‐related cirrhosis treated with direct‐acting antivirals
Bibliographic record
Abstract
BACKGROUND & AIMS: The risk of progression of indeterminate observations to hepatocellular carcinoma (HCC) after direct-acting antivirals (DAA) is still undetermined. To assess whether DAA therapy changes the risk of progression of observations with low (LR-2), intermediate (LR-3) and high (LR-4) probability for HCC in cirrhotic patients and to identify predictors of progression. METHODS: This retrospective study included cirrhotic patients treated with DAA who achieved sustained virological response between 2015 and 2019. A total of 68 patients had pre-DAA indeterminate observations and at least six months CT/MRI follow-up before and after DAA. Two radiologists reviewed CT/MRI studies to categorize observations according to the LI-RADSv2018 and assess the evolution on subsequent follow-ups. Predictors of evolutions were evaluated by using the Cox proportional hazard model, Kaplan-Meier method and log-rank test. RESULTS: A total of 109 untreated observations were evaluated, including 31 (28.4%) LR-2, 67 (61.5%) LR-3 and 11 (10.1%) LR-4. During a median follow-up of 41 months, 17.4% and 13.3% of observations evolved to LR-5 or LR-M and LR-5, before and after DAA respectively (P = .428). There was no difference in rate of progression of neither LR-2 (P = 1.000), LR-3 (P = .833) or LR-4 (P = .505). At multivariate analysis, only initial LI-RADS category was an independent predictor of progression to LR-5 or LR-M for all observations (hazard ratio 6.75, P < .001), and of progression to LR-5 after DAA (hazard ratio 4.34, P = .047). CONCLUSIONS: DAA therapy does not increase progression of indeterminate observations to malignant categories. The initial LI-RADS category is an independent predictor of observations upgrade.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".