Performance of preoperative plasma HE4 and CA-125 levels in predicting ovarian cancer mortality in women with epithelial ovarian cancer (EOC).
Bibliographic record
Abstract
e17076 Background: Additional prognostic biomarkers are needed to better manage women with EOC, especially dichotomized indicators for decision making. The objective of this external validation study was to assess the performance of preoperative plasma HE4 and CA-125 levels in predicting mortality by EOC. Methods: Eligible EOC women were newly diagnosed cases treated by upfront debulking surgery in a gynecology oncology center (CHU de Québec, L’Hôtel-Dieu, Canada) in 1988-2006 (cohort 1, n=136) and in 2007-2013 (cohort 2, n=177). All FIGO stages were included. Preoperative plasma HE4 and CA-125 levels were measured by Elecsys® automated immunoassay (Roche Diagnostics). Dates and causes of death were obtained by record linkage with the Quebec mortality files. In cohort 1, time-dependent receiver operating characteristic (ROC) curves were performed and optimal thresholds for HE4 and CA-125 were generated using the Youden index J. In cohort 2, crude and standardized Cox proportional models were done to validate the usefulness of these biomarkers according to their optimal thresholds. Standardized models included standard prognostic factors. The Likelihood Ratio (LR) tests were done to compare the standardized models with and without each biomarker. Results: In cohorts 1 and 2, medians of follow-up were respectively 5.3 and 3.2 years. Five-year disease free survival rates were 53% in cohort 1 and 54% in cohort 2. In cohort 1, the AUC for HE4 and CA-125 were respectively 64.2 (95% CI: 54.7-73.6) and 63.1 (95%CI: 53.6-72.6). The optimal thresholds were 277 pmol/L for HE4 and 282 U/ml for CA-125. In cohort 2, higher levels of plasma HE4 (≥277 pmol/L) were significantly associated with death by EOC (adjusted hazard ratio (aHR): 1.80; 95% CI: 1.03-3.15; p-value for LR test: 0.03), while higher levels of CA-125 (≥282 U/ml) were not associated with death by EOC (aHR: 1.50; 95% CI: 0.88-2.55; p-value for LR-test: 0.12). In serous EOC, the associations with mortality were respectively 2.46 (95% CI: 1.26-4.80) for HE4 and 1.56 (0.87-2.80) for CA-125. Conclusions: Preoperative plasma HE4 is a promising prognostic biomarker in women with EOC and performs better than CA-125 to predict mortality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".