Staining Performance of ALK and ROS1 Immunohistochemistry and Influence on Interpretation in Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
Selection of non-small-cell lung cancer patients for treatment relies on the detection of expression of anaplastic lymphoma kinase (ALK) and ROS proto-oncogene 1 (ROS1) protein by immunohistochemistry (IHC). We evaluated staining performance for different IHC protocols and laboratory characteristics, and their influence on ALK and ROS1 interpretation during external quality assessment schemes between 2015 and 2018. Participants received five formalin-fixed, paraffin-embedded cases for staining by their routine protocol, whereafter at least two pathologists scored them simultaneously under a multihead microscope and awarded a graded expert staining score (ESS) from 1 to 5 points based on staining quality. European Conformity in Vitro Diagnostic kits (such as D5F3) revealed a better ALK ESS compared with laboratory-developed tests. ESS was indifferent to the applied antibody dilution or a recent protocol change. Lower ESSs were observed for higher antibody incubation times and temperatures. ESS for various ROS1 protocols were largely similar. Overall, for both markers, ESS improved over time and for repeated external quality assessment participation but was independent of laboratory setting or experience. Except for ROS1, ESS positively correlated with laboratory accreditation. IHC stains with lower ESS correlated with increased error rates in ALK and ROS1 interpretation and analysis failures. Laboratory characteristics differently affected staining quality and interpretation, and laboratories should assess both aspects, and less common protocols need improvement in staining performance.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".