The Separation of Benign and Malignant Mesothelial Proliferations
Bibliographic record
Abstract
The separation of benign from malignant mesothelial proliferations is an important clinical but often a difficult morphologic problem. Over the last roughly 10 years a variety of new markers that aid in this separation have been published and some older recommended markers reconsidered. Unlike previous, and largely unusable, empiric immunohistochemical (IHC) stains, these new markers, some using IHC and some using fluourescent in situ hybridization (FISH), are largely based on documented genomic abnormalities in malignant mesotheliomas. However, no marker works in all situations; rather, markers need to be chosen by the morphology of the process in question (epithelial vs. spindled) and the body cavity of interest (pleural vs. peritoneal). It is also important to be familiar with the exact pattern, for example nuclear versus cytoplasmic loss, that indicates a positive test. Furthermore, no single marker is 100% sensitive even with the optimal morphology/location, so that combinations of markers are essential. This review covers the various new markers in the literature, highlights their advantages and limitations, and suggests morphology/site specific combinations that can produce sensitivities in the 80% to 90% (and perhaps higher) range. At present only BRCA-1 related protein-1 and methylthioadenosine phosphorylase IHC, and cyclin-dependent kinase inhibitor 2A (p16) FISH have sufficient publications and reproducibility of results to be considered as established markers. 5-Hydroxymethyl cytosine, enhancer of zeste homolog 2, cyclin D1, and programmed death-ligand 1 IHC, and NF2 FISH are all potentially useful but need further study. The newly described entity of malignant mesothelioma in situ sits at the interface of benign and malignant mesothelial process; criteria for this diagnosis are reviewed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".