Fine Needle Aspiration Cytology of Lobular Breast Carcinoma and Its Variants
Bibliographic record
Abstract
OBJECTIVE: To identify associations between cytological criteria in fine needle aspiration (FNA) specimens and histological subtypes of lobular breast carcinoma (classical and other types). STUDY DESIGN: FNA cytology and mastectomy specimens from 72 cases of invasive lobular breast carcinoma were consecutively retrieved from the files of the Amaral de Carvalho Hospital, Jaú-São Paulo, Brazil. All cases were reviewed regarding five cytological criteria: cellularity, cellular cohesion, presence of inflammation, nucleoli and nuclear atypia. The χ2 test or Fisher's exact tests with 95% confidence intervals (CI) were used. RESULTS: The classical type showed lower initial cytological diagnosis of malignancy compared to the other variants (p=0.017; odds ratio (OR) 0.26, 95% CI 0.89-0.80). Moderate/intense cellular cohesion (p=0.011; OR 0.18, 95% CI 0.04-0.73) and mild atypia (p=0.000; OR 16.15, 95% CI 3.20-81.48) were significantly associated with the classical type of lobular breast carcinoma, while the absence of inflammation (p=0.082; OR 0.36, 95% CI 0.12-1.15) was marginally associated with the classical type. CONCLUSIONS: In cytology, the characterization of lobular carcinoma as malignant is difficult, especially the classical type. The association between cell cohesion and the classical type of lobular breast carcinoma may be one of the factors that complicate this diagnosis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".