Abstract IA002: Histopathological classification of endometrial cancers and surrogate markers of genomic subtypes
Bibliographic record
Abstract
Abstract Endometrial carcinoma has been subclassified based on tumor cell-type morphology i.e. the resemblance of the tumor cells to their normal counterparts based on microscopic examination. The main histotypes of endometrial carcinoma, endometrioid and serous carcinoma, correspond roughly to Type I and Type II endometrial carcinoma, as described by Bokhman in 1983. Although these histotypes are associated with different patient outcomes and are associated with different mutations, with endometrioid carcinomas having a more favorable prognosis than serous carcinomas, there are a significant number of endometrial carcinomas that are difficult to classify based on histopathological examination. This results in there being only moderate inter-observer reproducibility in the histopathological classification of endometrial carcinoma and this, in turn, compromises the ability to use histotype as a basis for treatment decisions. The Cancer Genome Atlas identified 4 molecular subtypes of endometrial carcinoma based on genomic architecture: ultramutated, hypermutated, low numbers of somatic copy number abnormalities, and high numbers of somatic copy number abnormalities. These correlate with patient outcome and, increasingly, have been show to be correlated with response to treatment. In this presentation the current subclassification of endometrial carcinoma, the development of surrogate markers of genomic molecular subtype and the correlation between molecular subtype and histotype will be discussed,Endometrial carcinoma has been subclassified based on tumor cell-type morphology i.e. the resemblance of the tumor cells to their normal counterparts based on microscopic examination. The main histotypes of endometrial carcinoma, endometrioid and serous carcinoma, correspond roughly to Type I and Type II endometrial carcinoma, as described by Bokhman in 1983. Although these histotypes are associated with different patient outcomes and are associated with different mutations, with endometrioid carcinomas having a more favorable prognosis than serous carcinomas, there are a significant number of endometrial carcinomas that are difficult to classify based on histopathological examination. This results in there being only moderate inter-observer reproducibility in the histopathological classification of endometrial carcinoma and this, in turn, compromises the ability to use histotype as a basis for treatment decisions. The Cancer Genome Atlas identified 4 molecular subtypes of endometrial carcinoma based on genomic architecture: ultramutated, hypermutated, low numbers of somatic copy number abnormalities, and high numbers of somatic copy number abnormalities. These correlate with patient outcome and, increasingly, have been show to be correlated with response to treatment. In this presentation the current subclassification of endometrial carcinoma, the development of surrogate markers of genomic molecular subtype and the correlation between molecular subtype and histotype will be discussed. Citation Format: C. Blake Gilks. Histopathological classification of endometrial cancers and surrogate markers of genomic subtypes [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr IA002.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".