The challenge of managing isolated STIC lesions: A single-center experience
Bibliographic record
Abstract
• STIC lesions were found in 10 patients, of which five received adjuvant chemotherapy. • Progression to carcinoma was reported in one patient who did not receive chemotherapy. • Unilateral STIC lesions were found in 90% of patients. • Adjuvant chemotherapy was well tolerated but neuropathy led to dose adjustments in 40% of treated cases. High-grade serous carcinoma (HGSC) arise from serous tubal intraepithelial carcinoma (STIC) lesions, a precursor that develops from the fallopian tube epithelium. Patients with incidental isolated STIC lesions found on salpingectomy specimen have up to 25% risk of developing HGSC or peritoneal carcinomatosis in the future, yet there is no established consensus to guide management. This retrospective case series includes patients diagnosed with isolated STIC lesions between April 2017 and January 2024. Patient data was extracted from clinical and pathological databases. During the study period, 10 patients were diagnosed with an isolated STIC lesion. The fallopian tubes were removed either as part of a hysterectomy for endometrial cancer (n = 3); a prophylactic risk-reducing surgery for BRCA1 or BRCA2 mutation (n = 3); or a benign gynecologic condition (n = 4). The median age of the patients was 64 years (range: 53–80). Among patients who underwent genetic testing (n = 9), only three were found to have a deleterious germline mutation in BRCA1 or BRCA2 . The patients either received adjuvant chemotherapy (n = 5) or underwent active surveillance (n = 5). One surveillance patient was managed with completion bilateral oophorectomy and omentectomy. Median number of chemotherapy cycles was four (range 4–6 cycles). The median follow-up was 27 months (range: 5–83 months). One patient under active surveillance was diagnosed with peritoneal carcinomatosis 5 years after initial diagnosis of STIC whereas none recurred in the chemotherapy group. The wide variety of treatment approaches we observed highlights a need for more data on this entity to support management guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".