Uneven Staining in Automated Immunohistochemistry: Cold and Hot Zones and Implications for Immunohistochemical Analysis of Biopsy Specimens
Bibliographic record
Abstract
OBJECTIVES: The occurrence of uneven staining (UES) in automated immunohistochemistry (IHC) has been experienced by clinical laboratories and has the potential to confound readout, interpretation, and reporting of IHC assays despite the presence optimally stained on-slide controls. However, there are no studies of this phenomenon in regard to the type, frequency, and association with different automated IHC platforms. We studied the occurrence of UES in automated IHC assays with real world examples from clinical practice and by using a laboratory developed methodology to monitor baseline and periodic performance of automated IHC instruments. MATERIALS AND METHODS: Sections of formalin-fixed, paraffin-embedded normal liver tissue were mounted on 180 glass slides and stained for HepPar1 on 6 automated IHC instruments (4 different models from 3 different manufacturers). Macroscopic and microscopic defects of staining were recorded. RESULTS: Only 8% of slides showed completely uniform staining. UES, including areas of both increased and decreased staining, occurred with all instruments. Decreased staining was often zonal, involving large regions of the slide. Decreased staining mostly localized in an instrument-dependent manner. Increased staining tended to occur in small foci with a random distribution. CONCLUSIONS: The common occurrence of UES (particularly decreased staining) has important implications for the reliable read-out of IHC assays on biopsy samples. Baseline and periodic quality assurance testing for UES is recommended for all automated IHC instruments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".