Hypothesis: <scp>HEG1</scp> and claudin‐4 staining will allow a diagnosis of epithelioid and biphasic mesothelioma versus <scp>non‐small</scp>‐cell lung carcinoma with only two stains in most cases
Bibliographic record
Abstract
Separation of mesothelioma from metastatic carcinoma requires immunohistochemical support, with small batteries of stains recommended as a starting-point, but these numbers commonly expand to 10, 12 or more stains, a process that is not only expensive but frequently generates anomalous or confounding results, leading to even more stains. Here we review data on HEG1 clone SKM9-2, a new (now commercially available) mesothelioma marker and claudin-4, a broad-spectrum carcinoma marker, to ask whether these two stains are sufficient, by themselves, to separate mesotheliomas from non-small-cell lung (NSCLC) as well as other carcinomas. Data for HEG1, derived from four laboratories, showed membrane staining in 393 of 434 (91%) epithelioid/biphasic mesotheliomas and one of 360 (0.3%) NSCLC (sensitivity 91%, specificity 99.7%). Reports from seven laboratories evaluating claudin-4 in NSCLC showed positivity in 469 of 502 (93%) carcinomas and weak positivity in five of 463 (1.0%) epithelioid/biphasic mesotheliomas (sensitivity 93%, specificity 98.9%). Comparable results were found with carcinomas from other sites, except for serous and thyroid carcinomas, some of which react with HEG1 but are also positive for claudin-4. For sarcomatoid mesotheliomas, HEG1 sensitivity is modest and staining sometimes difficult to interpret. We hypothesise that the combination of HEG1 and claudin-4 immunostaining will potentially allow the separation of epithelioid/biphasic mesotheliomas from NSCLC carcinomas with high accuracy using only two immunostains in most cases. This combination will probably also work for carcinomas from most other sites, but more reports on HEG1 SKM9-2 staining of carcinomas other than NSCLC are needed. This approach would greatly simplify the diagnosis of mesothelioma.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".