Validation and optimization of AFP-based biomarker panels for early HCC detection in Latin America and Europe
Bibliographic record
Abstract
BACKGROUND: HCC is a major cause of cancer death worldwide. Serum biomarkers such as alpha-fetoprotein (AFP), protein induced by vitamin K absence-II, and the Gender, Age, AFP-L3, AFP, Des-gamma-carboxy prothrombin (GALAD) score have been recommended for HCC surveillance. However, inconsistent recommendations in international guidelines limit their clinical utility. METHODS: In this multicenter study, over 2000 patient samples were collected in 6 Latin American and 2 European countries. The performance of the GALAD score was validated in cirrhotic cases, and optimized versions were tested for early-stage HCC and prediagnostic HCC detection. RESULTS: The GALAD score could distinguish between HCC and cirrhosis in Latin American patients with an AUC of 0.76, sensitivity of 70%, and specificity of 83% at the conventional cutoff value of -0.63. In a European cohort, GALAD had an AUC of 0.69, sensitivity of 66%, and specificity of 72%. Optimizing the score in the 2 large multicenter cohorts revealed that AFP-L3 contributed minimally to early-stage HCC detection. Thus, we developed a modified GALAD score without AFP-L3, the ASAP (age, sex, AFP, and protein induced by vitamin K absence-II), which showed promise for early-stage HCC detection upon validation. The ASAP score also identified patients with cirrhosis at high risk for advanced-stage HCC up to 15 months before diagnosis (p < 0.0001) and differentiated HCC from hemangiomas, with a specificity of 100% at 71% sensitivity. CONCLUSION: Our comprehensive analysis of large sample cohorts validates the GALAD score's utility in Latin American, Spanish, and Dutch patients for early-stage HCC detection. The optimized GALAD without AFP-L3, the ASAP score, is a good alternative and shows greater promise for HCC prediction.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".