Serum Midkine for AFP-negative hepatocellular carcinoma diagnosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction To date, alpha-feto protein (AFP) remains the most widely used serum biomarker for hepatocellular carcinoma (HCC) diagnosis and prognosis. However, its role has become controversial as close to 30% of early stage HCC patients are AFP negative. Different studies on the diagnostic performance of novel AFP-negative HCC biomarkers have shown inconsistent results of sensitivity, specificity, and area under the receiver operating curve (AUROC). Here, we conducted a systematic review and meta-analysis to resolve this inconsistency. Methods Relevant studies were systematically searched from PubMed, Embase, Cochrane library, Scopus, and the China National Knowledge Infrastructure (chkd-cnki) databases up to the 20th October 2022. The Newcastle–Ottawa Scale was used to assess the methodological quality of included studies. Sensitivity, specificity, and area under the curve were pooled using the random effect model. Results Five studies, with a total of 286 patients, were included. Serum Midkine was assessed using enzyme-linked immunosorbent assay (ELISA) in all the studies, at diagnostic thresholds varying from 0.387 to 5.1 ng/ml. The summary estimates for serum Midkine were 76% (95% CI 70–81%) sensitivity, 85% (95% CI 82–87%) specificity, and 91% area under the receiver operating characteristic curve (AUC), while the pooled diagnostic odds ratio (DOR) was 27.64 (95% CI 4.95–154.17). Conclusion Based on these findings, serum Midkine is a very promising diagnostic biomarker for AFP-negative HCC and should be validated further in large cohort studies.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".