Protein Induced by Vitamin K Absence or Antagonist-II Versus Alpha-Fetoprotein in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review With Meta-Analysis
Bibliographic record
Abstract
Background: Protein induced by vitamin K absence or antagonist-II (PIVKA-II) and α-fetoprotein (AFP) are promising tumor markers for the diagnosis of hepatocellular carcinoma (HCC). Yet, their diagnostic performance differs throughout HCC investigations. The aim of this meta-analysis was to assess the effectiveness of PIVKA-II and AFP in the diagnosis of HCC. Methods: A systematic literature search was performed to identify relevant studies from eight databases, which were published up to February 2023, in order to compare the diagnostic performance of PIVKA-II and AFP for HCC. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic (SROC) curve was performed to assess the diagnostic accuracy of each biomarker. Results: Fifty-three studies were identified. The pooled sensitivity (95% confidence interval (CI)) of PIVKA-II and AFP was 0.71 (0.70 - 0.72) and 0.64 (0.63 - 0.65), respectively in diagnosis of HCC, and the corresponding pooled specificity (95% CI) was 0.90 (0.89 - 0.90) and 0.87 (0.87 - 0.88), respectively. The area under the ROC curve (AUC) of PIVKA-II and AFP was 0.89 (0.88 - 0.90) and 0.78 (0.77 - 0.79), respectively. Subgroup analysis demonstrated that PIVKA-II presented higher AUC values compared to AFP in terms of ethnic group (African, European, Asian, and American patients), etiology (mixed-type HCC, hepatitis C virus (HCV)-related, and hepatitis B virus (HBV)-related) and sample size of cases (≤ 100 and > 100). Conclusion: This study reveals that PIVKA-II is a promising biomarker for identifying and tracking HCC, exhibiting greater accuracy than AFP. Our findings indicate that PIVKA-II outperforms AFP in detecting HCC across diverse racial groups and sample sizes, as well as in cases of HBV-related, HCV-related, or mixed-etiology HCC.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".