The evolving role of axillary lymph node fine‐needle aspiration in the management of carcinoma of the breast
Bibliographic record
Abstract
BACKGROUND: Image-guided fine-needle aspiration (FNA) studies of axillary lymph nodes (LN) to evaluate breast carcinoma have shown high specificity but variable sensitivity. The purposes of this study were to evaluate the performance of axillary LN FNA depending on clinicoradiologic findings and to document how treatment varied according to FNA results. METHODS: The study cohort consisted of consecutive axillary LN FNA cases over a 4-year period, in which subsequent treatment was known. Clinicoradiologic assessment was classified as "low suspicion" or "high suspicion" and cytopathologic findings as "positive," "negative," or "indeterminate". The test performance for each, using surgical pathology outcome as the "gold standard," was calculated. The impact of axillary LN FNA on subsequent management decisions was analyzed. RESULTS: Of the 163 cases, axillary FNA was positive in 94 of 163 (58%), negative in 55 of 163 (34%), and atypical/nondiagnostic in 14 of 163 (8%). A clinicoradiologic assessment of "high suspicion" had a positive predictive value (PPV) of 88%, whereas a "low suspicion" assessment had a negative predictive value (NPV) of only 68%. In contrast, the PPV and NPV of axillary LN FNA were 98.7% and 81.8%, respectively. Whereas all of the FNA-nonpositive cases were managed surgically, surgery was deferred in 26 of 94 of the FNA-positive cases, including 11 cases of neoadjuvant treatment. Most of the remaining (65 of 68) FNA-positive patients were spared sentinel lymph node biopsy. CONCLUSIONS: Image-guided LN FNA is highly sensitive and specific for lymph node involvement by breast carcinoma and plays a role both in sparing sentinel lymph node biopsy and in triaging cases for systemic therapy.
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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 | 0.012 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".