Adjuvant Treatment of Stage I–II Serous Endometrial Cancer: A Single Institution 20-Year Experience
Bibliographic record
Abstract
Background: Serous endometrial carcinoma (SEC) is a high-risk subtype of endometrial cancer. The effectiveness of multiple adjuvant therapies, namely chemotherapy (CT), radiotherapy (RT), and sequential/concurrent chemotherapy with radiotherapy (CRT), have previously been investigated. However, optimal management of early-stage SEC remains unclarified. Methods: All cases of early-stage SEC (FIGO 2009 stages I–II) treated in our institution from 2002 to 2019 were identified. Patient data were documented until September 2023. Overall survival (OS) and disease-free survival (DFS) were computed using Kaplan–Meier estimates and Cox’s proportional hazard model; descriptive statistical analysis was performed. Results: A total of 50 patients underwent total hysterectomy-bilateral salpingo-oophorectomy and omentectomy, displaying stage IA (60%), IB (24%), and II (16%) disease. The median follow-up was 90.9 months. Patients underwent adjuvant CRT (n = 36, 72%), CT (n = 6, 12%), or RT (n = 6, 12%). Two patients were observed and excluded from analyses. The 42 patients who received radiotherapy had pelvic external beam radiotherapy (n = 10), vaginal brachytherapy (n = 21), or both (n = 11). CRT had better OS (HR 0.14, 95%CI 0.04–0.52, p < 0.005) and DFS (HR 0.25, 95%CI 0.07–0.97, p = 0.05) than CT alone. RT displayed no OS or DFS benefits compared to CT/CRT. Recurrences were mostly distant. Acute and late G3-4 toxicities were primarily hematologic. Conclusions: Our data underline the challenge of treating SEC. CRT appears to be superior to CT alone but not to RT. Most recurrences were distant, highlighting the need for optimized systemic treatment options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".