Endometrial Cancer: Transitioning from Histology to Genomics
Bibliographic record
Abstract
Endometrial carcinoma (EC) is traditionally treated with surgery and adjuvant treatment depending on clinicopathological risk factors. The genomic analysis of EC in 2013 and subsequent studies using immunohistochemistry have led to the current EC molecular classification into: polymerase epsilon mutated (POLEmut), p53 abnormal (p53abn), mismatch repair deficient (MMRd), and no specific molecular profile (NSMP). The four groups have prognostic value and represent a promising tool for clinical decision-making regarding adjuvant treatment. Molecular classification was integrated into the recent European Society of Gynecologic Oncology (ESGO) management guidelines. POLEmut EC has favorable outcomes and retrospective studies found that omitting adjuvant treatment is safe in this group; two prospective clinical trials, PORTEC-4 and TAPER, are ongoing to assess this. p53 abn is associated with increased recurrence, decreased survival, and benefitted from chemotherapy in the PORTEC-3 subgroup molecular analysis. The clinical trials PORTEC-4a and CANSTAMP will prospectively assess this. MMRd and NSMP groups have intermediate prognosis and will likely continue to rely closely on clinicopathological features for adjuvant treatment decisions. In addition, the molecular classification has led to exploring novel treatments such as checkpoint inhibitors. Overall, the molecular perspective on EC and associated clinical trials will likely refine EC risk stratification to optimize care and avoid overtreatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".