Mismatch repair deficiency as a predictor of adjuvant radiotherapy response in endometrioid endometrial carcinoma.
Bibliographic record
Abstract
5586 Background: Adjuvant radiotherapy improves progression-free survival in intermediate and high-risk endometrial cancer. However, so far there is no evidence of improved overall or disease-specific survival after adjuvant radiotherapy. There is accumulating evidence that MMR proteins are involved in DNA repair following radiotherapy. We investigated the predictive value of MMR status in terms of survival benefit after adjuvant radiotherapy in patients with stage IB/II, grade 3 endometrioid endometrial cancer (EEC). Methods: A retrospective multicenter cohort study was performed to compare patients with histopathologically confirmed stage IB/II grade 3 EEC with and without adjuvant radiotherapy. Patients were classified according to the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) identifying ECs as either MMR-deficient, POLE, p53abn or p53wt. Multivariable Cox regression analysis explored associations between patient characteristics, adjuvant treatment and outcome. Results: A total of 128 patients were analyzed, including 57 patients (43.0%) with MMR-deficient EECs. Baseline characteristics were comparable, except a higher proportion of MMR-deficient EECs were stage II (36.8% vs. 15.5%, p = 0.006). Eighty-two patients (64.1%) received adjuvant radiotherapy (external beam [n = 55], vaginal brachytherapy [n = 27]). In multivariate analysis, adjuvant radiotherapy was independently associated with improved disease-specific survival in patients with MMR-deficient EECs (hazard ratio 0.19, 95%-CI 0.05 - 0.77), but not in patients with MMR-proficient EECs (hazard ratio 0.92, 95%-CI 0.37 - 2.31). Conclusions: Adjuvant radiotherapy improved disease-specific survival in patients with MMR-deficient EECs, but not in those with MMR-proficient EECs. This study demonstrates the predictive ability of MMR IHC to identify women who likely have increased benefit from radiotherapy.
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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) | 0.000 | 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) | 0.001 | 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".