HER2 Testing and Clinical Decision Making in Gastroesophageal Adenocarcinoma: Guideline From the College of American Pathologists, American Society for Clinical Pathology, and American Society of Clinical Oncology
Bibliographic record
Abstract
CONTEXT: - ERBB2 (erb-b2 receptor tyrosine kinase 2 or HER2) is currently the only biomarker established for selection of a specific therapy for patients with advanced gastroesophageal adenocarcinoma (GEA). However, there are no comprehensive guidelines for the assessment of HER2 in patients with GEA. OBJECTIVES: - To establish an evidence-based guideline for HER2 testing in patients with GEA, to formalize the algorithms for methods to improve the accuracy of HER2 testing while addressing which patients and tumor specimens are appropriate, and to provide guidance on clinical decision making. DESIGN: - The College of American Pathologists, American Society for Clinical Pathology, and American Society of Clinical Oncology convened an expert panel to conduct a systematic review of the literature to develop an evidence-based guideline with recommendations for optimal HER2 testing in patients with GEA. RESULTS: - The panel is proposing 11 recommendations with strong agreement from the open-comment participants. RECOMMENDATIONS: - The panel recommends that tumor specimen(s) from all patients with advanced GEA, who are candidates for HER2-targeted therapy, should be assessed for HER2 status before the initiation of HER2-targeted therapy. Clinicians should offer combination chemotherapy and a HER2-targeted agent as initial therapy for all patients with HER2-positive advanced GEA. For pathologists, guidance is provided for morphologic selection of neoplastic tissue, testing algorithms, scoring methods, interpretation and reporting of results, and laboratory quality assurance. CONCLUSIONS: - This guideline provides specific recommendations for assessment of HER2 in patients with advanced GEA while addressing pertinent technical issues and clinical implications of the results.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".