Assessment of Thin-Layer Breast Aspirates for Immunocytochemical Evaluation of HER2 Status
Bibliographic record
Abstract
OBJECTIVE: To determine whether immunocytochemistry (ICC) for HER2 on ThinPrep (TP)-processed breast fine needle aspiration biopsies (Cytyc Corp., Boxborough, Massachusetts, U.S.A.) is comparable to the findings of immunohistochemistry on corresponding surgically removed tissue. STUDY DESIGN: Immunostaining was performed on 63 malignant breast fine needle aspirates and compared to immunostaining on paraffin sections (PSs) from the subsequent biopsies. The HercepTest (Dako, Carpinteria, California, U.S.A.) and TAB250 antibodies were utilized. Cases in which the TP and paraffin HER2 results did not correlate were further assessed for gene amplification by differential polymerase chain reaction (dPCR). RESULTS: HER2 overexpression was found in 9 of the 63 cases (14%). TAB250 had higher specificity on PS versus TP (P = .008), and TAB250 had higher specificity on PS versus the HercepTest on PS and TP (P = .004 and .0001, respectively). CONCLUSION: HER2 immunostaining with both the HercepTest and TAB250 on TP is unreliable due to low specificity (72% and 83% for HercepTest and TAB250, respectively). However, both antibodies have high sensitivity (89% and 100%, respectively); suggesting that this method may have some utility as a preliminary screening test for HER2 status. Negative HER2 staining by ICC is highly predictive of the absence of HER2 overexpression, whereas positive HER2 staining on TP would require further validation by either dPCR of fluorescence in situ hybridization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".