The Accuracy of Subclassifying Poorly Differentiated Non-Small Cell Lung Carcinoma Biopsies With Commonly Used Lung Carcinoma Markers
Bibliographic record
Abstract
Background: The recent development of targeted therapies has magnified the importance of differentiating between squamous cell carcinoma (SCC) and adenocarcinoma (AC) on biopsy specimens. The biopsy is a fractional representation of the whole tumor, and the small sample size often makes architectural interpretation difficult. Recent literature has applied immunohistochemical staining (IHC) to attempt to subclassify non-small cell lung cancer (NSCLC). The aim of this study was to assess the accuracy of IHC in subtyping poorly differentiated NSCLCs on biopsy when compared to their corresponding surgical resections. Methods: Twenty-four cases of NSCLC that could not be subclassified on hematoxylin and eosin stained biopsy specimens alone and had subsequent resection specimens were identified. Resection specimens were considered the gold standard, and the tumor was classified based on World Health Organization criteria. All biopsies and resection specimens were reviewed along with any IHC markers that were used to aid diagnosis. Results: Eighteen of the 24 cases (75%) had no change in diagnosis following primary tumor resection. Of the 6 biopsies that had an alternate final diagnosis, 4 were originally classified as NSCLC not otherwise specified (NOS) and subsequently subclassified to either AC (3 cases) or SCC (1 case). One SCC was changed to adenosquamous carcinoma and one AC was changed to large cell lung carcinoma (LCLC) due to focal Napsin staining on the biopsy that was discounted on the resection. The IHC staining patterns were 100% congruent between the biopsies and surgical resection specimens. The majority of diagnosis changes were due to architectural features that become more apparent on the larger resection specimens. Conclusions: IHC staining using a combination of lung carcinoma markers allows accurate subclassification of poorly differentiated NSCLCs on biopsies in most cases. Surgical specimens allow for further subclassification mainly due to architectural features that are more apparent on whole resection.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".