New Markers for Separating Benign From Malignant Mesothelial Proliferations: Are We There Yet?
Bibliographic record
Abstract
CONTEXT: The separation of benign from malignant mesothelial proliferations is crucial to patient care but is frequently morphologically difficult. OBJECTIVE: To briefly review adjunctive tests claimed to be useful in this setting and to examine in detail 2 new tests: p16 fluorescence in situ hybridization (FISH) and BRCA1-associated protein 1 (BAP1) immunohistochemistry. DESIGN: Literature review with emphasis on p16 FISH and BAP1 immunohistochemistry. RESULTS: Glucose transporter-1, p53, insulin-like growth factor 2 messenger RNA-binding protein 3 (IMP-3), desmin, and epithelial membrane antigen have all been claimed to mark either benign or malignant mesothelial processes, but in practice they at best provide statistical differences in large series of cases, without being useful in an individual case. Homozygous deletion of p16 by FISH or loss of BAP1 has only been reported in malignant mesotheliomas and not in benign mesothelial proliferations. BAP1 appears to be lost more frequently in epithelial than mixed or sarcomatous mesotheliomas. Homozygous deletion of p16 by FISH is seen in pleural epithelial, mixed, and sarcomatous mesotheliomas, but it is much less frequent in peritoneal mesothelioma. The major drawback to both these tests is limited sensitivity; moreover, failure to find p16 deletion or BAP1 loss does not make a mesothelial process benign. CONCLUSIONS: In the context of a mesothelial proliferation, the finding of homozygous deletion of p16 by FISH or loss of BAP1 by immunohistochemistry is, thus far, 100% specific for malignant mesothelioma. The limited sensitivity of each test may be improved to some extent by running both tests.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".