Interlaboratory Concordance of ProMisE Molecular Classification of Endometrial Carcinoma Based on Endometrial Biopsy Specimens
Bibliographic record
Abstract
Molecular classifiers improve the consistency of categorization of endometrial carcinoma and provide valuable prognostic information. We aimed to evaluate the interlaboratory agreement in ProMisE assignment across 3 dedicated Canadian gynecologic oncology centers. Fifty cases of endometrial carcinoma diagnosed on biopsy were collected from 3 centers and 3 unstained sections were provided to each participating site so that immunohistochemistry for MSH6, PMS2, and p53 could be performed and interpreted at each center, blinded to the original diagnoses and the results from other centers. A core was taken for DNA extraction and POLE mutation testing. Overall accuracy and κ statistic were assessed. MSH6, PMS2, and p53 could be assessed for all 50 cases, with agreement for 140/150 results. There was a high level of agreement in molecular classification (κ=0.82), overall. Cases with a discordant result for one of the features used in classification (n=10) were reviewed independently and the most common reason for disagreement was attributable to the weak p53 staining in 1 laboratory (n=4). Interpretive error in PMS2 (n=1) and MSH6 (n=2) assessment accounted for 3 of the remaining disagreements. Interpretive error in the assessment of p53 was identified in 2 cases, with very faint p53 nuclear reactivity being misinterpreted as wild-type staining. These results show strong interlaboratory agreement and the potential for greater agreement if technical and interpretive factors are addressed. Several solutions could improve concordance: central quality control to ensure technical consistency in immunohistochemical staining, education to decrease interpretation errors, and the use of secondary molecular testing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".