Ovarian cancer distribution of histology, stage, and screening performance.
Bibliographic record
Abstract
5543 Background: Survival in women with ovarian cancer is strongly influenced by stage of disease at diagnosis. As such, strategies have been investigated to identify biomarkers for early detection. This premise assumes a progression of disease from early to late stage. Varying histologic subtypes in ovarian cancer have distinct etiologies. It is likely that early detection strategies will need to be subtype specific. This study sought to evaluate histologic subtypes, stage of disease, and screening performance in a cohort of women diagnosed with ovarian cancer. Methods: This analysis was performed as an REB approved sub-study of a single institution ovarian cancer tumor banking protocol for which all patients presenting with suspected ovarian cancer, since February 2011, were eligible. This analysis included all patients with confirmed ovarian cancer. Patients were identified and tracked prospectively. Results: There were 135 patients with ovarian cancer (mean age 57 ± 12 years). 67% were post-menopausal. The distribution of histologic subtypes was 42% high-grade serous (HGS), 18% endometrioid, 12% clear cell, 6% low-grade serous (LGS), 6% sex-cord stromal, 5% germ cell, 3% mucinous, 3% mixed, 3% carcinosarcoma, and 2% other. 64 (47%) women presented with advanced disease with a median CA 125 of 260 (range 14 – 21 782), of whom 43 (68%) were found to have HGS histology. 46 (34%) patients presented with stage I disease with a median CA 125 of 41 (range 3 – 9305). Of these, the distribution of histologic subtypes included 13 (28%) endometrioid, 9 (20%) clear cell, 5 (11%) sex-cord stromal, 5 (11%) HGS, 3 (7%) mucinous and 23% other. Risk of Malignancy Index (RMI) scoring for women with stage I disease (N = 35) revealed a false negative rate of 31%, including 4 clear cell, 2 LGS, 2 endometrioid and 1 each of HGS, mucinous, and mixed (clear cell and endometrioid) histologies. Conclusions: Stage I ovarian cancer consists primarily of non-serous histologies, which are not reliably detected using CA 125 and ultrasound markers. Current approaches to screening for early stage disease may require the identification of biomarkers unique to clear cell and endometrioid histologies and novel strategies for the identification of patients at risk for HGS carcinomas.
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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 | 0.002 |
| 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) | 0.003 | 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".