Histotype-Genotype Correlation in 36 High-grade Endometrial Carcinomas
Bibliographic record
Abstract
Endometrioid, serous, and clear cell carcinomas are the major types of endometrial carcinoma. Histologic distinction between these different tumor types can be difficult in high-grade cases, in which significant interobserver diagnostic disagreement exists. Endometrioid and clear cell carcinomas frequently harbor ARID1A and/or PTEN mutations. Serous carcinoma acquires TP53 mutations/inactivation at onset, with a significant subset harboring an additional mutation in PPP2R1A. This study examines the correlation between tumor histotype and genotype in 36 previously genotyped high-grade endometrial carcinomas. This included 23 endometrioid/clear cell genotype and 13 serous genotype tumors. Eight subspecialty pathologists reviewed representative online slides and rendered diagnoses before and after receiving p53, p16, and estrogen receptor immunostaining results. κ statistics for histotype-genotype concordance were calculated. The average κ values for histotype-genotype concordance was 0.55 (range, 0.30 to 0.67) on the basis of morphologic evaluation alone and it improved to 0.68 (range, 0.54 to 0.81) after immunophenotype consideration (P<0.001). Genotype-incompatible diagnoses were rendered by at least 2 pathologists in 12 of 36 cases (33%) (3 cases by 2/8 pathologists, 2 by 3/8, 2 by 4/8, 3 by 6/8, 1 by 7/8, and 1 case by 8/8 pathologists). Six of the 12 were endometrioid/clear cell genotype tumors, and the other 6 were serous genotype tumors. The histopathologic features associated with histotype-genotype-discordant cases were reviewed, and specific diagnostic recommendations were made to improve concordance. This study found that although the majority of morphologic diagnoses are genotype concordant, genotype-incompatible diagnoses are made in a significant subset of cases. Judicious use and interpretation of p53 immunohistochemistry in selected scenarios can improve histotype-genotype concordance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".