HtrA1 expression and the prognosis of high-grade serous ovarian carcinoma: a cohort study using digital analysis
Bibliographic record
Abstract
BACKGROUND: The expression of high temperature requirement factor A1 (Htra1) has been reported to be decreased in ovarian carcinoma, but its prognostic effect remains undetermined. METHODS: We evaluated the impact of HtrA1 downregulation in tumoral tissues on cancer progression and death in women with serous ovarian carcinoma. HtrA1 staining was performed on tissue microarrays (TMA) comprised of tumor samples from a cohort of 106 women who were diagnosed with primary high-grade serous ovarian carcinoma and receiving standard treatment at the Québec University Hospital between 1993 and 2006. HtrA1 expression was assessed visually (percentage of positive nuclei) and by digital image analysis (percentage of positive area). Cox regression multivariate models included standard prognostic factors and were used to estimate adjusted hazard ratios (aHR) for progression or death in the cohort. RESULTS: By visual analysis, a low percentage of HtrA1-positive nuclei (< 10% vs ≥10%) tend to be associated with a lower risk of progression (aHR = 0.71; 95% Confidence interval (CI) = 0.46-1.09; P = 0.11) and mortality (aHR = 0.65; 95% CI = 0.41-1.04; P = 0.07). Low nuclear HtrA1 expression assessed by digital image analysis (< median % vs ≥ median %) showed a significant association with lower risk of progression (aHR = 0.62; 95% CI = 0.40-0.95; p = 0.03) and death (aHR = 0.60; 95% CI = 0.38-0.95; p = 0.03). CONCLUSION: Altogether, our results demonstrate that nuclear downregulation of HtrA1 is associated with a better prognosis in women with high grade serous ovarian carcinoma.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".