Calibration and Optimization of p53, WT1, and Napsin A Immunohistochemistry Ancillary Tests for Histotyping of Ovarian Carcinoma
Bibliographic record
Abstract
The Canadian Immunohistochemistry Quality Control provides proficiency testing for immunohistochemistry in Canadian laboratories. Canadian Immunohistochemistry Quality Control Run 42 assessed WT1, Napsin A, and p53; commonly used markers for histotyping ovarian carcinomas. A 42-core tissue microarray, which included the 5 major histotypes of ovarian carcinomas with a subset having known TP53 mutational status, was used for this Canadian Immunohistochemistry Quality Control challenge. Participants included 43 laboratories for p53, 29 for WT1, and 26 for Napsin A. p53 was scored as aberrant if the staining was strong and diffuse or absent. Napsin A and WT1 were scored positive if any tumor cells stained. The reference p53 expression pattern was inferred by TP53 mutation type when available. For WT1, Napsin A, and cases lacking mutational data, the reference staining pattern was based on the majority staining result. The error rate for p53 was 8.8%. Most errors (84%) were due to weak staining. The sensitivity and specificity of aberrant p53 expression for an underlying TP53 mutation was 91.6% and 87.9%, respectively. The error rate for WT1 was 0.76% with all errors occurring in laboratories using the 6F-h2 clone. The average errors for laboratories using 6F-h2 were 2.4 compared with 0 for WT-49. The error rate for Napsin A was 4%. The average errors for laboratories using polyclonal Napsin A were 3 compared with 1.1 for monoclonal Napsin A. Weak p53 staining increases interpretative errors, primarily due to absence of staining in tumors with wild-type TP53. p53 immunohistochemistry correlates strongly with TP53 mutational status. Polyclonal Napsin A and 6F-h2 may lack specificity in comparison to monoclonal Napsin A and WT-49.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".