HER2 In Situ Hybridization in Gastric and Gastroesophageal Adenocarcinoma
Bibliographic record
Abstract
Patients with gastric and gastroesophageal junction (GEJ) adenocarcinomas that are HER2 positive by immunohistochemistry (IHC) or in situ hybridization show a significant survival benefit with trastuzumab therapy. In situ hybridization is traditionally done by fluorescence in situ hybridization (FISH), despite some limitations. An alternative is the dual in situ hybridization (Dual ISH) technique that is fully automated and uses differentially labeled CEP17 and HER2 probes that can be read by light microscopy on 1 slide. The aim of this study was to assess the utility of Dual ISH in gastric/GEJ cancer and to compare the results with those obtained by IHC and FISH. Cases of gastric/GEJ adenocarcinoma were analyzed by IHC, FISH, and Dual ISH and the correlation between methods calculated. Results for 50 patients were available. There was a 98% (49/50) concordance rate between Dual ISH and FISH. One discrepant case was nonamplified by FISH but showed focal amplification by Dual ISH. Discrepancy was attributed to tumor heterogeneity, which was a frequent finding (78% of HER2-positive cases). There was excellent correlation between Dual ISH and FISH for assessment of HER2 amplification. Dual ISH was rapid, easy to interpret, and maintained cell morphology, which was valuable in identifying tumor heterogeneity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".