Utility of a CEA, CD15, Calretinin, and CK5/6 Panel for Distinguishing Between Mesotheliomas and Pulmonary Adenocarcinomas in Clinical Practice
Bibliographic record
Abstract
Most reports on antibodies that claimed to separate mesothelioma from pulmonary adenocarcinoma originated from academic centers or specialized immunohistochemistry laboratories, but little is known about how such stains perform in general practice laboratories. The Canadian Immunohistochemistry Quality Control program circulates tissue array slides to laboratories across Canada; these are stained and then interpreted by the local laboratory and by a set of experienced reviewers. For Canadian Immunohistochemistry Quality Control run 16, tissue array slides from 16 pulmonary adenocarcinomas and 6 mesotheliomas were stained in 36 different laboratories for CEA, CD15, CK5/6, and calretinin. A total of 736 results (cores) were interpretable. If 3 of 4 staining results concordant with the diagnosis was accepted as definitive, 166/192 (86.4%) mesothelioma cores and 461/544 (84.7%) adenocarcinoma cores were correctly diagnosed. However, if 4 of 4 concordant markers were required, then 93/192 (48.4%) mesothelioma cores and 265/544 (48.7%) adenocarcinoma cores were correctly diagnosed. Only 3/192 (1.6%) mesothelioma cores were incorrectly classified as carcinomas and 8/544 (1.5%) of adenocarcinoma cores incorrectly classified as mesotheliomas on the basis of the immunoprofile (ie, 3 of 4 or 4 of 4 marker results were discordant with the diagnosis). We conclude that, in a study based on results from nonspecialized laboratories, the combination of CEA, CD15, calretinin, and CK5/6, used as a panel, has a very low false-positive rate when separating pulmonary adenocarcinomas from mesotheliomas; however, single negative or incorrect results are common, therefore the panel is only useful diagnostically if 3 of 4 correct results are deemed acceptable for diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".