Diagnostic Variation in p53 Usage for Endometrial Carcinoma Diagnosis: Implications for Molecular Subtyping
Bibliographic record
Abstract
Immunostaining for p53 is widely but variably used when diagnosing endometrial carcinoma (EC). Mutant-pattern p53 staining can support a diagnosis of serous carcinoma, and also serve as a surrogate test for identifying the "serous-like" subset of aggressive EC identified by The Cancer Genome Atlas characterized by high numbers of somatic copy number abnormalities. We, retrospectively, assessed WHO histotype, usage of p53 immunostaining, and p53 status in a consecutive series of biopsies showing EC from a single hospital. Of 79 ECs, 59 (75%) were low-grade EC (LGEC), 13 (16%) high-grade EC (HGEC), and 7 (9%) were serous. p53 immunostaining was performed at the time of diagnosis in 27/79 (34%) biopsies; 6/7 of serous histotype, 11/13 HGEC, and 10/59 LGEC. Mutant-pattern p53 staining was present in 6/6 serous, 2/11 HGEC, and 2/10 LGEC. The remaining 53 tumors subsequently had p53 immunostaining done; all 49 LGEC showed wild-type staining and the serous carcinoma and 1/2 HGEC showed mutant pattern staining. While there are no guidelines on using p53 in endometrial biopsies, this study shows consistent usage in high-grade histotypes and variable usage in LGEC. As 100% (7/7) of serous EC and 3% (2/59) of the LGECs showed mutant-pattern p53 staining, histotype may serve as a surrogate for p53 assessment, such that only HGEC or ambiguous carcinomas should be routinely subjected to p53 immunostaining.
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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".