Inter-observer reproducibility of HER2 immunohistochemical assessment and concordance with fluorescent in situhybridization (FISH): pathologist assessment compared to quantitative image analysis
Bibliographic record
Abstract
BACKGROUND: In breast cancer patients, HER2 overexpression is routinely assessed by immunohistochemistry (IHC) and equivocal cases are subject to fluorescent in situ hybridization (FISH). Our study compares HER2 scoring by histopathologists with automated quantitation of staining, and determines the concordance of IHC scores with FISH results. METHODS: A tissue microarray was constructed from 1,212 invasive breast carcinoma cases with linked treatment and outcome information. IHC slides were semi-quantitatively scored by two independent pathologists on a range of 0 to 3+, and also analyzed with an Ariol automated system by two operators. 616 cases were scorable by both IHC and FISH. RESULTS: Using data from unequivocal positive (3+) or negative (0, 1+) results, both visual and automated scores were highly consistent: there was excellent concordance between two pathologists (kappa = 1.000, 95% CI: 1-1), between two machines (kappa = 1.000, 95% CI: 1-1), and between both visual and both machine scores (kappa = 0.898, 95% CI: 0.775-0.979). Two pathologists successfully distinguished negative, positive and equivocal cases (kappa = 0.929, 95% CI: 0.909-0.946), with excellent agreement with machine 1 scores (kappa = 0.835, 95% CI: 0.806-0.862; kappa = 0.837, 95% CI: 0.81-0.862), and good agreement with machine 2 scores (kappa = 0.698, 95% CI: 0.6723-0.723; kappa = 0.709, 95% CI: 0.684-0.732), whereas the two machines showed good agreement (kappa = 0.806, 95% CI: 0.785-0.826). When comparing categorized IHC scores and FISH results, the agreement was excellent for visual 1 (kappa = 0.814, 95% CI: 0.768-0.856), good for visual 2 (kappa = 0.763, 95% CI: 0.712-0.81) and machine 1 (kappa = 0.665, 95% CI: 0.609-0.718), and moderate for machine 2 (kappa = 0.535, 95% CI: 0.485-0.584). CONCLUSION: A fully automated image analysis system run by an experienced operator can provide results consistent with visual HER2 scoring. Further development of such systems will likely improve the accuracy of detection and categorization of membranous staining, making this technique suitable for use in quality assurance programs and eventually in clinical practice.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".