Do Risk Factors for HCC Impact the Association of CT/MRI LIRADS Major Features With HCC? An Individual Participant Data Meta-Analysis
Bibliographic record
Abstract
Background: Guidelines suggest the Liver Imaging Reporting and Data System (LI-RADS) may not be applicable for some populations at risk for hepatocellular carcinoma (HCC). However, data assessing the association of HCC risk factors with LI-RADS major features are lacking. Purpose: To evaluate whether the association between HCC risk factors and each CT/MRI LI-RADS major feature differs among individuals at-risk for HCC. Methods: Databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched from 2014 to 2022. Individual participant data (IPD) were extracted from studies evaluating HCC diagnosis using CT/MRI LI-RADS and reporting HCC risk factors. IPD from studies were pooled and modelled with one-stage meta-regressions. Interactions were assessed between major features and HCC risk factors, including age, sex, cirrhosis, chronic hepatitis B virus (HBV), and study location. A mixed effects model that included the major features, as well as separate models that included interactions between each risk factor and each major feature, were fit. Differences in interactions across levels of each risk factor were calculated using adjusted odds-ratios (ORs), 95% confidence-intervals (CI), and z -tests. Risk of bias was assessed using QUADAS-2. (Protocol: https://osf.io/tdv7j/ ). Results: Across 23 studies (2958 patients and 3553 observations), the associations between LI-RADS major features and HCC were consistent across several HCC risk factors ( P -value range: .09-.99). A sensitivity analysis among the 4 studies with a low risk of bias did not differ from the primary analysis. Conclusion: The association between CT/MRI LI-RADS major features and HCC risk factors do not significantly differ in individuals at-risk for HCC. These findings suggest that CT/MR LI-RADS should be applied to all patients considered at risk by LI-RADS without modification or exclusions, regardless of the presence or absence of the risk factors evaluated in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.074 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".