Magnetic resonance imaging-based rim enhancement could effectually predict poor prognosis in hepatocellular carcinoma: a meta-analysis
Bibliographic record
Abstract
Recent studies have initially shown that MRI-based rim enhancement associates with poor prognosis in hepatocellular carcinoma (HCC) patients, but their sample sizes are small, leading to a necessary of comprehensive analyses to make a relatively solid statement. Thus, this meta-analysis aimed to summarize the correlation between MRI-based rim enhancement and prognosis in HCC patients. Until March 2023, a literature search was conducted on Web of Science, PubMed, EMBASE, Cochrane, CNKI, Wangfang, and CQVIP databases in order to identify studies that report the correlation between MRI-based rim enhancement and the prognosis of HCC patients. MRI-based rim enhancement and prognostic data were extracted and analyzed. In our study, eight studies containing 1816 HCC patients were analyzed. Generally, the presence of MRI-based rim enhancement was related to shortened disease-free survival (DFS) [hazard ratio (HR): 2.77, 95% confidence interval (CI): 2.11-3.62, P < 0.001], and worse overall survival (OS) (HR: 5.43, 95% CI: 2.14-13.79, P < 0.001). While no other prognostic data could be retrieved. Funnel plots, Begg's test, and Egger's test all indicated that no publication bias existed; and the risk score by Newcastle-Ottawa Scale criteria ranged from 7-9 points, suggesting a generally low risk of bias. Meanwhile, the sensitivity analysis showed that the significant findings did not change by omitting each study. Then, subgroup analyses revealed that no matter stratified by tumor size, treatment option, or sample size, rim enhancement was linked with unsatisfied DFS (all P < 0.05). Conclusively, MRI-based rim enhancement could effectually estimate poor survival in HCC patients, indicating its good prognostic value.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.052 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".