Molecular Classification Guides Fertility-Sparing Treatment for Endometrial Cancer and Atypical Hyperplasia Patients
Bibliographic record
Abstract
Objectives: The objective of this study was to investigate the significance of molecular classification in guiding treatment decisions for patients with endometrial cancer (EC) or atypical hyperplasia (AH) undergoing fertility-sparing treatment (FST), particularly for those with non-NSMP subtypes. Methods: We conducted a retrospective cohort study involving EC/AH patients undergoing FST and molecular classification using next-generation sequencing at Peking University People’s Hospital between June 2020 and September 2023. Results: A total of 118 EC/AH patients were included, including 92 cases with NSMP, 11 with MMRd, 11 with POLEmut, and 4 with p53abn. (1) Of the 11 patients with MMRd, 6 achieved a complete response (CR) with 1 case receiving progestin, 3 cases showed insensitivity to the initial progestin before transitioning to a combined regimen of progestin and a PD-1 inhibitor, and 2 cases initially received progestin plus a PD-1 inhibitor. There were no significant differences in the cumulative CR rates between the MMRd and NSMP subgroups but a trend of a lower relapse-free-survival (RFS) rate for the MMRd subgroup (p = 0.074). (2) Of the 11 cases with POLEmut, 10 achieved CR but 4 relapsed. There was also a trend for a lower RFS rate in the POLEmut patients (p = 0.069) compared with the NSMP subgroup. (3) Three of the four patients with p53mut achieved CR after treatment with the GnRHa plus LNG-IUS regimen. Conclusion: The selection of appropriate regimens may improve FST outcomes in EC/AH patients with molecular classification of non-NSMP subtypes. Immunotherapy is an effective fertility-preserving approach for patients with MMRd.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".