Performance of preoperative plasma tumor markers HE4 and CA125 in predicting ovarian cancer mortality in women with epithelial ovarian cancer
Bibliographic record
Abstract
Objective Clinical utility of new biomarkers often requires the identification of their optimal threshold. This external validation study was conducted to assess the performance of the preoperative plasma tumor markers HE4 and CA125 optimal cut‐offs to predict cancer mortality in women with epithelial ovarian cancer (EOC). Methods Participating women had upfront debulking surgery in the University Hospital of Quebec City (Canada) between 1988 and 2013. A total of 136 women participated in the training cohort (cohort 1) and 177 in the validation cohort (cohort 2). Preoperative plasma HE4 and CA125 levels were measured by Elecsys®. Optimal thresholds were identified in the cohort 1 using time‐dependent receiver operating characteristic (ROC) curves. Multivariate Cox models were used to validate the biomarkers using their optimal cut‐offs in the cohort 2. The likelihood ratio (LR) test was done to test the prognostic value of each biomarker. Results The Areas Under the Curves (AUC) for HE4 and CA125 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 CA125. Only HE4 (≥277 pmol/L) was significantly associated with EOC mortality (adjusted hazard ratio: 1.80; 95% CI: 1.03–3.15). In addition, HE4 added prediction for death over the standard prognostic markers (p‐value for LR‐test: 0.03). Conclusions Preoperative HE4 is a promising prognostic biomarker and performs better in predicting EOC mortality than CA125. This prognostic information provided by this optimal HE4 cut‐off, before surgery, could be useful for clinicians to better plan treatment strategies. Support or Funding Information This study was partly funded by the “Banque de tissus et de données du Réseau de recherche sur le cancer, Fonds de Recherche du Québec‐Santé », which is affiliated with the Canadian Tumor Repository Network. We thank Roche Diagnostics (Laval, Canada) for providing the HE4 and CA125 kits and the funds for the dosages and the statistical analyses. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.007 |
| 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".