Chromogenic and Silver in Situ Hybridization for Identification of HER 2 Overexpression in Breast Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: This systematic review has the purpose to characterize the accuracy of chromogenic in situ hybridization (CISH) and silver in situ hybridization (SISH), in comparison to fluorescence in situ hybridization (FISH) in the identification of human epidermal growth factor receptor-2 (HER2) overexpression and to inform decisions about test selection. MATERIALS AND METHODS: We searched MEDLINE and EMBASE databases using these eligibility criteria: studies evaluating invasive breast cancer samples which examined agreement between CISH or SISH, and FISH, and reported sensitivity, specificity, or concordance. We performed a bivariate meta-analysis of sensitivity and specificity using a generalized linear mixed model. We used likelihood ratio tests from meta-regression to compare accuracy between HER2 tests. RESULTS: The search identified 4475 articles, of which 32 were included. The summary estimates for sensitivity and specificity were 0.91 [95% confidence interval (CI), 0.85-0.95], and 0.97 (95% CI, 0.93-0.99) for SISH; 0.97 (95% CI, 0.83-1.00) and 0.99 (95% CI, 0.96-1.00) for single-probe CISH; and, 0.98 (95% CI, 0.92-0.99) and 0.98 (95% CI, 0.91-0.99) for dual-probe CISH. Significantly higher sensitivity was reported for dual-CISH than SISH (χ: 5.36; P=0.02) when compared with the reference test FISH. CONCLUSIONS: The agreement between new bright field tests (SISH and CISH) and FISH is high (≥92%). Indirect comparison of HER2 tests indicated that overall CISH performance exceeds that of SISH. The pooled estimates from this meta-analysis summarize the current published literature and, in addition to other factors such as costs differentials, can help inform future HER2 test selection decisions.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".