Hematoxylin and Eosin Counterstaining Protocol for Immunohistochemistry Interpretation and Diagnosis
Bibliographic record
Abstract
Hematoxylin and eosin (H&E) staining is a well-established technique in histopathology. However, immunohistochemistry (IHC) interpretation is done exclusively with hematoxylin counterstaining. Our goal was to investigate the potential of H&E as counterstaining (H&E-IHC) to allow for visualization of a marker while confirming the diagnosis on the same slide. The quality of immunostaining and the fast-technical performance were the main criteria to select the final protocol. We stained multiple diagnostic tissues with class I IHC tests with different subcellular localization markers (anti-CK7, CK20, synaptophysin, CD20, HMB45, and Ki-67) and with double-staining on prostate tissues with anti-high molecular weight keratins/p63 (DAB detection) and p504s (alkaline phosphatase detection). To validate the efficacy of the counterstaining, we stained tissue microarrays from the Canadian Immunohistochemistry Quality Control (cIQc) with class II IHC tests (ER, PR, HER2, and p53 markers). Interobserver and intraobserver concordance was assessed by κ statistics. Excellent agreement of H&E-IHC interpretation was observed in comparison with standard IHC from our laboratory (κ, 0.87 to 1.00), and with the cIQc reference values (κ, 0.81 to 1.00). Interobserver and intraobserver agreement was excellent (κ, 0.89 to 1.00 and 0.87 to 1.00, respectively). We therefore show for the first time the potential of using H&E counterstaining for IHC interpretation. We recommend the H&E-IHC protocol to enhance diagnostic precision for the clinical workflow and research studies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.022 |
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".