Proficiency Testing of p53 Immunohistochemistry Pattern Read-out in Vulvar Biopsies Demonstrates Frequent Basal Overexpression Interpretation in TP53 Wild-type Cases
Bibliographic record
Abstract
Recently, criteria for p53 immunohistochemistry (IHC) interpretation were described in squamous neoplasia of the vulva. This pattern-based approach detailed 2 wild-type patterns (scattered and basal-sparing) and 4 mutant patterns (parabasal/diffuse overexpression, basal overexpression, null, and cytoplasmic). However, the proficiency of pathologist read-out has not been studied. We created an online tool to evaluate p53 IHC interpretation proficiency. p53 IHC on 90 vulvar biopsies (n=31 squamous insitu /premalignant and n=59 benign lesions) were scanned (without corresponding H&E). Fifteen pathologists assessed 45 cases in Module A and assigned each case as wild-type or mutant via the 6 p53 IHC patterns. Following Module A, participants were given the suggested p53 IHC pattern and TP53 sequencing data for each case. After self-review, pathologists completed a second 45 case set (Module B). The average pathologist score per case increased from Module A to Module B (69.8%-87.7%, P =0.0005). Pathologist proficiency was excellent in the parabasal/diffuse (100%-100%), null (93.3%-90.0%), and basal-sparing (88.9%-100%) patterns. The greatest discrepancy was due to the interpretation of the basal overexpression pattern in cases that were TP53 wild-type by sequencing, but this improved with educational intervention. Scores for the scattered pattern improved from 64.9% to 82.8% and basal overexpression from 73.3% to 91.1% after completion of the training module. Pathologists should exhibit caution when interpreting p53 IHC as basal overexpression, as this pattern can be seen in the absence of TP53 alterations. There were 2 cases with convincing p53 IHC abnormal patterns (1 parabasal/diffuse and 1 null) without TP53 mutations by sequencing.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".