Optimal Immunohistochemical Markers For Distinguishing Lung Adenocarcinomas From Squamous Cell Carcinomas in Small Tumor Samples
Bibliographic record
Abstract
The histologic subtype of non-small cell lung carcinoma is important in selecting appropriate chemotherapy for patients with advanced disease. As many of these patients are not operative candidates, they are treated medically after biopsy for diagnosis. Inherent limitations of small biopsy samples can make distinguishing poorly differentiated lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) difficult. The value of histochemical and immunohistochemical markers to help separate poorly differentiated ADC from SCC in resection specimens is well established; however, the optimal use of markers in small tissue samples has only recently been examined and the correlation of marker expression in small tissue samples with histologic subtype determined on resection specimens has not been well documented. We address this issue by examining the expression of 9 markers (p63, TTF1, CK5/6, CK7, 34βE12, Napsin A, mucicarmine, NTRK1, and NTRK2) on 200 cases of ADC and 225 cases of SCC in tissue microarray format to mimic small tissue specimens. The single best marker to separate ADC from SCC is p63 (for SCC: sensitivity 84%, specificity 85%). Logistic regression analysis identifies p63, TTF1, CK5/6, CK7, Napsin A, and mucicarmine as the optimal panel to separate ADC from SCC. Reduction of the panel to p63, TTF1, CK5/6, and CK7 is marginally less effective but may be the best compromise when tissue is limited. We present an algorithm for the stepwise application of p63, TTF1, CK5/6, CK7, Napsin A, and mucicarmine in situations in which separation of ADC from SCC in small specimens cannot be accomplished by morphology alone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".