Selection of endometrial carcinomas for <scp>p53</scp> immunohistochemistry based on nuclear features
Bibliographic record
Abstract
The World Health Organization endorses molecular subclassification of endometrial endometrioid carcinomas (EECs). Our objectives were to test the sensitivity of tumor morphology in capturing p53 abnormal (p53abn) cases and to model the impact of p53abn on changes to ESGO/ESTRO/ESP (European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology) risk stratification. A total of 292 consecutive endometrial carcinoma resections received at Foothills Medical Centre, Calgary, Canada (2019-2021) were retrieved and assigned to ESGO risk groups with and without p53 status. Three pathologists reviewed the representative H&E-stained slides, predicted the p53 status, and indicated whether p53 immunohistochemistry (IHC) would be ordered. Population-based survival for endometrial carcinomas diagnosed during 2008-2016 in Alberta was obtained from the Alberta Cancer Registry. The cohort consisted mostly of grade 1/2 endometrioid carcinomas (EEC1/2; N = 218, 74.6%). One hundred and fifty-two EEC1/2 (52.1% overall) were stage IA and 147 (50.3%) were low risk by ESGO. The overall prevalence of p53abn and subclonal p53 was 14.5 and 8.3%, respectively. The average sensitivity of predicting p53abn among observers was 83.6%. Observers requested p53 IHC for 39.4% with 98.5% sensitivity to detect p53abn (99.6% negative predictive value). Nuclear features including smudged chromatin, pleomorphism, atypical mitoses, and tumor giant cells accurately predicted p53abn. In 7/292 (2.4%), p53abn upgraded ESGO risk groups (2 to intermediate risk, 5 to high risk). EEC1/2/stage IA patients had an excellent disease-specific 5-year survival of 98.5%. Pathologists can select cases for p53 testing with high sensitivity and low risk of false negativity. Molecular characterization of endometrial carcinomas has great potential to refine ESGO risk classification for a small subset but offers little value for approximately half of endometrial carcinomas, namely, EEC1/2/stage IA cases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".