Nonsmall Cell Lung Carcinoma With Neuroendocrine Differentiation—An Entity of No Clinical or Prognostic Significance
Bibliographic record
Abstract
The existence of non-small cell lung carcinoma with neuroendocrine differentiation as a distinct entity and its relevance for prognostic and treatment purposes is controversial. This study assesses the frequency and biologic and prognostic significance of neuroendocrine (NE) expression of synaptophysin (SNP), chromogranin (Ch), and neural cell adhesion molecule (N-CAM) using tissue microarray (TMA) and immunohistochemistry. Six hundred nine nonsmall cell lung carcinomas (NSCLCs) were reviewed for subclassification. TMA blocks were made using duplicate 0.6-mm-diameter tissue cores and slides stained with SNP, Ch, and N-CAM. Immunoreactivity was considered if 1% or more of tumor cells were positive. Hematoxylin and eosin-stained sections were subclassified as: 243 adenocarcinoma (ACA), 272 squamous cell carcinoma (SCC), 35 large cell carcinoma, 32 non-small cell carcinoma NOS, and 6 other (carcinosarcoma, giant cell carcinoma). Positivity for either marker was identified in 13.6% of NSCLC (76/558). NSCLC showed reactivity for Ch in 0.4% of cases (2/524), for SNP in 7.5% of cases (39/521) and for N-CAM in 8.6% of cases (44/511), whereas only 0.2% of cases (1/517) showed coexpression of SNP and Ch and none of all 3 markers. The assessment of NE differentiation in NSCLC is unnecessary and expensive and is of no clinical or prognostic significance. SNP or N-CAM stains a small minority of NSCLC, whereas Ch immunoreactivity is less common. Positivity for any 2 NE markers is rare. SNP is more likely to be expressed in adenocarcinoma (P=0.01) and N-CAM in squamous-cell carcinoma (P=0.008). Otherwise there was no correlation between immunoreactivity and tumor morphology. Disease specific and overall survival is not influenced by NE differentiation and therefore non-small cell lung carcinoma with neuroendocrine differentiation should not be a subclass distinct from the other NSCLC.
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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 | 0.001 |
| 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 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".