HER2/neu Testing in Gastric Cancer by Immunohistochemistry: Assessment of Interlaboratory Variation
Bibliographic record
Abstract
CONTEXT: Immunohistochemical (IHC) testing for HER2/neu is becoming the standard of care for guiding adjuvant treatment of gastric carcinoma with trastuzumab. OBJECTIVE: To assess interlaboratory variation in IHC staining and interpretation across multiple laboratories. DESIGN: A tissue microarray consisting of 45 cores from 28 gastric cancers was distributed to 37 laboratories for HER2/neu assessment. The IHC results were compared against expert scores at an academic institution and correlated with in situ hybridization results from the originating specimen. Interlaboratory agreement was calculated using Cohen κ statistic. RESULTS: The survey demonstrated several variations in IHC methods, including the primary antibodies in use. There was excellent agreement among laboratories in HER2/neu(+) (IHC 3(+)) cases (κ = 0.80 ± 0.01) and very good agreement among laboratories in HER2/neu(-) (IHC 0 or 1(+)) cases (κ = 0.58 ± 0.01). Less agreement was observed among laboratories when scoring equivocal (IHC 2(+)) cases (κ = 0.22 ± 0.01). Sensitivity and specificity of HER2/neu IHC were 99% and 100%, respectively, when measured against expert review and consensus score as a reference standard. CONCLUSIONS: There is substantial interlaboratory agreement in the interpretation of HER2/neu IHC despite variability in protocols. Although HER2/neu IHC is a highly sensitive and specific test, primary antibody selection may significantly affect IHC results. Furthermore, gastric tumors require a unique scoring system and expertise in interpretation. Intratumoral heterogeneity has a significant effect on HER2/neu scoring by IHC. Ongoing quality assurance exercises among laboratories will help ensure optimized HER2/neu testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".