Endometrial carcinoma: molecular subtypes, precursors and the role of pathology in early diagnosis
Bibliographic record
Abstract
Endometrial carcinoma (EC) is classified into a wide range of morphological variants; this list has expanded over the past decade with the inclusion of mesonephric-like and dedifferentiated carcinoma as EC variants in the fifth edition of the WHO Classification of Female Genital Tumours, and recognition that carcinosarcoma is a biphasic carcinoma rather than a sarcoma. Each EC variant has distinct molecular abnormalities, including TCGA-based molecular subtypes, allowing further subclassification and adding complexity. In contrast to this rapid progress in understanding EC, there are only two recognized EC precursor lesions: endometrial atypical hyperplasia/endometrioid intraepithelial neoplasia (EAH/EIN) and serous intraepithelial carcinoma, a situation that has not changed for many years. Diagnosis of EC precursors is a cornerstone of surgical pathology practice, with early diagnosis contributing to the relatively favorable prognosis of EC. In this review we relate the precursor lesions to each of the EC morphological variants and molecular subtypes, discuss how successful early diagnosis is for each variant/molecular subtype and how it might be improved, and identify knowledge gaps where there is insufficient understanding of EC histogenesis. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".