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Record W2982402876 · doi:10.21873/anticanres.11701

Concordance of HER2 Immunohistochemistry and Fluorescence In Situ Hybridization Using Tissue Microarray in Breast Cancer

2017· article· en· W2982402876 on OpenAlexafffund
Daniela Furrer, Simon Jacob, Chantal Caron, François Sanschagrin, Louise Provencher, Caroline Diorio

Bibliographic record

VenueAnticancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsHôpital du Saint-SacrementUniversité Laval
FundersFonds de Recherche du Québec - SantéFondation du cancer du sein du QuébecUniversité Laval
KeywordsConcordanceImmunohistochemistryTissue microarrayFluorescence in situ hybridizationBreast cancerPathologyIn situ hybridizationMedicineCancerBiologyOncologyInternal medicineGene expressionGene

Abstract

fetched live from OpenAlex

AIM: Immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are common methods for assessment of human epidermal growth factor receptor 2 (HER2) in breast cancer. MATERIALS AND METHODS: In a cohort of 498 consecutive patients with breast cancer, we examined concordance between IHC and FISH for HER2 on tissue microarray (TMA) sections. In a subset of 116 specimens, we examined HER2 concordance from the block used for diagnostics and a randomly-chosen additional block (a proxy of the core biopsy). RESULTS: Overall concordance between both methods on TMA sections was 93.8% and between HER2, determined on diagnostic and additional blocks, was 93.6% for IHC and 98.0% for FISH. CONCLUSION: Since some cases were discordant, we suggest that both methods be used for HER2 assessment. The lower concordance rate between diagnostic and additional blocks using IHC compared to FISH suggests a greater variability of IHC staining across tumor regions than for FISH results.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.081
GPT teacher head0.487
Teacher spread0.405 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2017
Admission routes2
Has abstractyes

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