Fine-needle aspiration biopsy of bronchioloalveolar carcinoma
Bibliographic record
Abstract
BACKGROUND: The purpose of the current study was to determine the accuracy of the cytologic diagnosis of bronchioloalveolar carcinoma (BAC) by fine-needle aspiration biopsy (FNAB). METHODS: During a 4-year period (1994-1998), 1664 lung FNABs were performed. Forty-nine patients with BAC diagnosed by FNAB and/or surgical biopsy formed the basis of this study. RESULTS: Twenty-four patients diagnosed with BAC by FNAB had histologic confirmation. Surgical pathology revealed BAC in 15 patients with a cytologic diagnosis of large cell carcinoma (LCA) or adenocarcinoma (ACA). Nine patients diagnosed with BAC by FNAB were found to have ACA histologically. One unsatisfactory aspirate was diagnosed as BAC by surgical pathology. Review of 15 FNAB specimens with a diagnosis of LCA or ACA revealed cytologic features typical of BAC. In six aspirates, additional features such as pronounced nuclear crowding and overlapping, variation in nuclear size, and increased number of pleomorphic cells interfered with the FNAB diagnosis of BAC. Nine FNABs with a diagnosis of BAC were found histologically to have ACA with a focal BAC growth pattern. One unsatisfactory FNAB aspirate diagnosed as BAC histologically was due to sampling error. CONCLUSIONS: A diagnosis of BAC by FNAB is possible using conventional cytologic criteria. Some BACs show pronounced nuclear crowding and overlapping, variation in nuclear size, and an increased number of pleomorphic cells cytologically, which may interfere with an FNAB diagnosis of BAC. FNABs from ACA cases with a focal BAC pattern remain a diagnostic dilemma due to the nature of the lesion. In addition, sampling error by FNAB can be a diagnostic pitfall. Cancer (Cancer Cytopathol)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".