Nuclear β‐catenin and <scp>CDX</scp>2 expression in ovarian endometrioid carcinoma identify patients with favourable outcome
Bibliographic record
Abstract
AIMS: Ovarian endometrioid carcinoma (EC) generally has a good prognosis. Adjuvant chemotherapy can be spared in low-stage disease, but prognostic biomarkers are needed to refine the treatment threshold. Wnt/β-catenin signalling is commonly altered in EC. We examined immunohistochemical expression of nuclear β-catenin and CDX2 as prognostic biomarkers for EC; both are mediators of the Wnt pathway. METHODS AND RESULTS: We evaluated two ovarian EC cohorts, discovery set (n = 183) and validation set (n = 174), with ovarian cancer-specific survival (OCSS) as the primary end-point. In univariable analysis, nuclear β-catenin expression was significantly associated with longer OCSS in the discovery set [hazard ratio (HR) = 0.36, 95% confidence interval (CI) = 0.16-0.74, P = 0.004] and the validation set (HR = 0.35, 95% CI = 0.11-0.89, P = 0.006). Similar significant associations were observed with CDX2 expression in the discovery set (HR = 0.25, 95% CI = 0.11-0.50, P < 0.001) and validation set (HR = 0.27, 95% CI = 0.07-0.75, P = 0.020). In multivariable analysis, combined positivity of both markers was significantly associated with longer OCSS in the discovery set (HR = 0.20, 95% CI = 0.06-0.49, P < 0.001) and in the validation set (HR = 0.33 95% CI = 0.07-0.1.06, P = 0.047). In a stratified analysis for stage IC/II EC, combined positivity identified a subset of patients with a significantly longer OCSS in the discovery cohort but only a non-significant trend in the validation cohort. CONCLUSIONS: Nuclear β-catenin and CDX2 expression individually or in combination are validated prognostic markers for ovarian EC. However, their full potential to stratify low risk patients at adjuvant threshold awaits further multimarker study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".