Diagnostic accuracy of serum derived exosomes for hepatocellular carcinoma: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Early and non-invasive detection of hepatocellular carcinoma (HCC), which is usually asymptomatic, can improve overall survival outcomes. The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of serum-derived exosomes for diagnosing HCC. METHODS: PubMed, Web of Science, and Scopus databases were searched for relevant studies up to April 2023. The quality of included studies was assessed using the QUADAS-2 checklist, and data were extracted. Statistical analysis was performed on 18 studies from 3,993 records, and a diagnostic meta-analysis was conducted. Biomarkers were categorized into four groups based on their type (exosomal miRNAs, exosomal RNAs, alpha-fetoprotein (AFP), and exosomal RNAs+AFP panel), and a meta-analysis was conducted for each category separately. RESULTS: The highest pooled sensitivity was 0.86 for exosomal miRNAs, and exosomal RNAs+AFP had the highest pooled specificity; (0.89). Furthermore, exosomal RNAs+AFP had the highest pooled positive likelihood ratio; (7.55), the highest pooled diagnostic odds ratio (35.96) and the highest pooled area under the curve (0.93). Exosomal miRNAs had the lowest pooled negative likelihood ratio; (0.17). CONCLUSIONS: The diagnostic accuracy of exosomal biomarkers is superior to that of AFP, and combining the two in a panel yields the better results.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".