The value of ‘mesothelium‐associated’ antibodies in distinguishing between metastatic renal cell carcinomas and mesotheliomas
Bibliographic record
Abstract
AIMS: Despite increasing usage of mesothelium-associated antibodies in diagnosis, a meta-analysis of studies analysing these antibodies in relation to distinguishing mesothelioma from renal cell carcinoma shows a paucity of published data. Given the clinical importance of elucidating this differential diagnosis, we compared the phenotypes of these two tumours using a panel of antibodies comprising recently described 'mesothelium-associated' antibodies and the more established 'epithelium-associated' antibodies. METHODS AND RESULTS: We applied an antibody panel comprising calretinin, cytokeratin (CK)5/6, thrombomodulin, carcinoembryonic antigen (CEA), BerEP4 and BCA225 to 37 cases of pleural mesotheliomas and 40 cases of renal cell carcinoma (27 primary tumours and 13 metastatic to the pleura). All mesotheliomas were either purely epithelioid or of mixed type. Cases of renal cell carcinoma were graded and classified as to cell type and architecture. For mesotheliomas, 0% stained for CEA, 16% for BerEP4, 83% for BCA225, 78% for CK5/6, 86% for thrombomodulin and 97% showed nuclear staining for calretinin. For renal cell carcinomas, 0% stained for CEA, 50% for BerEP4, 88% for BCA225, 5% for CK5/6, 32% for thrombomodulin and 10% showed nuclear staining for calretinin. CONCLUSION: Calretinin, CK5/6 and BerEP4 appear the most useful antibodies in helping to distinguish between renal cell carcinomas and mesotheliomas, although BerEP4 was not particularly sensitive for renal cell carcinomas. Thrombomodulin was not as specific as the other 'mesothelium-associated' antibodies in this study, reflecting how staining for mesothelium-associated antibodies varies in carcinomas from different primary sites, and such variations should be taken into account when assessing the differential diagnosis of mesothelioma. In cases where doubt remains over distinguishing metastatic renal cell carcinoma from mesothelioma, data from such a panel should be viewed with caution and assessed in association with clinical, imaging and morphological features.
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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) | 0.000 | 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) | 0.000 | 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".