Association of Hormone Receptor Expression with Survival in Ovarian Endometrioid Carcinoma: Biological Validation and Clinical Implications
Bibliographic record
Abstract
This paper aims to validate whether hormone receptor expression is associated with longer survival among women diagnosed with ovarian endometrioid carcinoma (EC), and whether it identifies patients with stage IC/II tumors with excellent outcome that could be spared from toxic chemotherapy. Expression of estrogen receptor (ER) and progesterone receptor (PR) was assessed on 182 EC samples represented on tissue microarrays using the Alberta Ovarian Tumor Type (AOVT) cohort. Statistical analyses were performed to test for associations with ovarian cancer specific survival. ER or PR expression was present in 87.3% and 86.7% of cases, respectively, with co-expression present in 83.0%. Expression of each of the hormonal receptors was significantly higher in low-grade tumors and tumors with squamous differentiation. Expression of ER (Hazard Ratio (HR) = 0.18, 95% confidence interval 0.08–0.42, p = 0.0002) and of PR (HR = 0.22, 95% confidence interval 0.10–0.53, p = 0.0011) were significantly associated with longer ovarian cancer specific survival adjusted for age, grade, treatment center, stage, and residual disease. However, the five-year ovarian cancer specific survival among women with ER positive stage IC/II EC was 89.0% (standard error 3.3%) and for PR positive tumors 89.9% (standard error 3.2%), robustly below the 95% threshold where adjuvant therapy could be avoided. We validated the association of hormone receptor expression with ovarian cancer specific survival independent of standard predictors in an independent sample set of EC. The high ER/PR co-expression frequency and the survival difference support further testing of the efficacy of hormonal therapy in hormone receptor-positive ovarian EC. The clinical utility to identify a group of women diagnosed with EC at stage IC/II that could be spared from adjuvant therapy is limited.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".