Canadian Management of Serous Tubal Intraepithelial Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To assess the management and outcomes of patients diagnosed with an isolated serous tubal intraepithelial carcinoma lesion across Canada. METHODS: This retrospective study included consecutive patients with an isolated serous tubal intraepithelial carcinoma lesion diagnosed between 2006 and 2020 at 15 Canadian centers. Cases underwent multicenter panel pathology review. RESULTS: Of 107 patients, 41 serous tubal intraepithelial carcinoma cases (38.3%) were identified at prophylactic surgery for germline pathogenic variants, 36 (33.6%) at surgery for suspicion of malignancy, and 30 (28.0%) at surgery for benign conditions. Treatment groups included observation (n=62, 57.9%), staging surgery (n=35, 32.7%), and adjuvant chemotherapy (n=10, 9.3%). Median follow-up was 55.5 months (interquartile range 30.26-82.07 months). Overall, nine patients developed high-grade serous carcinoma. The cumulative incidence of high-grade serous carcinoma was not significantly different between treatment groups ( P =.181); however, no patient treated with chemotherapy developed high-grade serous carcinoma. The cumulative incidence of high-grade serous carcinoma was 1.1% (95% CI, 0.1-5.3%) at 2 years and 5.7% (95% CI, 1.8-13.1%) at 5 years. No significant predictive factors were found on univariate analysis. After multicenter pathology review of 59 cases (55.1%), consensus diagnosis was reached: 45 (76.3%) with serous tubal intraepithelial carcinoma, three (5.1%) with serous tubal intraepithelial lesion, seven (11.9%) with high-grade serous carcinoma, and two (3.4%) with normal tissue. Of the cases reviewed, only 1 of 45 patients (2.2%) with confirmed serous tubal intraepithelial carcinoma developed high-grade serous carcinoma at 73 months, indicating a 5-year cumulative incidence of cancer of 2.6% (95% CI, 0.2-11.7). CONCLUSION: Management of serous tubal intraepithelial carcinoma varied across centers. The 5-year cumulative incidence of high-grade serous carcinoma after isolated serous tubal intraepithelial carcinoma was 5.7%, consistent with recent literature. However, multicenter pathology review revealed initial underdiagnosed high-grade serous carcinoma, and 5-year cumulative incidence of high-grade serous carcinoma after confirmed serous tubal intraepithelial carcinoma decreased to 2.6%, underscoring the importance of diagnostic confirmation by expert pathologists to guide accurate management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".