Fallopian Tube Lesions in Women at High Risk for Ovarian Cancer: A Multicenter Study
Bibliographic record
Abstract
Abstract The prognosis of women diagnosed with invasive high-grade serous ovarian carcinoma (HGSC) is poor. More information about serous tubal intraepithelial carcinoma (STIC) and serous tubal intraepithelial lesions (STIL), putative precursor lesions of HGSC, could inform prevention efforts. We conducted a multicenter study to identify risk/protective factors associated with STIC/STILs and characterize p53 signatures in the fallopian tube. The fallopian tubes and ovaries of 479 high-risk women ≥30 years of age who underwent bilateral risk-reducing salpingo-oophorectomy were reviewed for invasive cancer/STICs/STILs. Epidemiologic data was available for 400 of these women. In 105 women, extensive sampling of the tubes for STICs/STILs/p53 signatures were undertaken. Descriptive statistics were used to compare groups with and without lesions. The combined prevalence of unique tubal lesions [invasive serous cancer (n = 6) /STICs (n = 14)/STILs (n = 5)] was 6.3% and this was split equally among BRCA1 (3.0%) and BRCA2 mutation carriers (3.3%). A diagnosis of invasive cancer was associated with older age but no risk/protective factor was significantly associated with STICs/STILs. Extensive sampling identified double the number of STICs/STILs (11.9%), many p53 signatures (27.0%), and multiple lesions in 50% of the cases. Women with p53 signatures in the fimbria were older than women with signatures in the remaining tube (P = 0.03). STICs/STILs may not share the protective factors that are associated with HGSC. It is plausible that these factors are only associated with STICs that progress to HGSC. Having multiple lesions in the fimbria may be an important predictor of disease progression. Cancer Prev Res; 11(11); 697–706. ©2018 AACR.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".