Do fine needle aspirate cytomorphological features correlate with positron emission tomography findings of metastatic non‐small cell lung carcinoma in lymph nodes?
Bibliographic record
Abstract
BACKGROUND: Our objective was to correlate cytomorphological features of metastatic non-small cell lung carcinoma (mNSCLC) with maximal standardized uptake value (mSUV) of positron emission tomography (PET) in Lymph nodes (LNs). METHODS: Positive cytology slides of 114 LNs were reviewed from 100 patients with mNSCLC who had undergone PET study. Student's t-test was used for statistical comparisons. RESULTS: Mean patients' age: 68.5, 54% male. LNs locations were: mediastinum: 99, lung hilum: 13, peribronchial: 1, axilla: 1. Final diagnoses were: Adenocarcinoma: 86, squamous cell carcinoma: 28 LNs. Within the adenocarcinoma subgroup, histological patterns correlate with mSUV. Acinar and papillary patterns were associated with significantly lower mSUVs (mean ± standard error (SE): 7.9 ± 0.9 and 9.2 ± 0.8, respectively) than solid pattern (13.0 ± 1.2; p values: 0.001 and 0.009, respectively). Similar difference exists between patterns associated with low- and high-grade adenocarcinoma (Mean ± SE: 9.2 ± 0.8 and 12.0 ± 1.0, respectively. p value: 0.02). Interestingly, micropapillary pattern was associated with the lowest mSUV amongst all patterns (Mean ± SE: 5.4 ± 1.1). Other features that correlated with higher mSUV were necrosis, moderate/severe nuclear atypia, lower lymphoid tissue yield, and contralateral LN involvement. CONCLUSIONS: In LNs with mNSCLC, certain cytomorphological features are associated with higher mSUV. Micropapillary, a pattern considered as high-grade, is associated with lower SUV values; hence, a lower SUV threshold may raise concern for metastasis. Although high SUV is associated with LN metastasis, lower SUV levels in certain adenocarcinomas suggest correlation with clinical and morphological characteristics could be valuable in tailoring therapeutic management.
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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 | 0.000 | 0.004 |
| 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".