Use of Programmed Death Ligand-1 (PD-L1) Staining to Separate Sarcomatoid Malignant Mesotheliomas From Benign Mesothelial Reactions
Bibliographic record
Abstract
Context.— The separation of benign from malignant mesothelial proliferations is a difficult morphologic problem. Some mesotheliomas stain for programmed death ligand-1 (PD-L1). Objective.— To determine whether PD-L1 staining can separate mesotheliomas from reactive mesothelial proliferations (RMPs). Design.— We stained 2 tissue microarrays containing in toto 62 malignant mesotheliomas and 88 RMPs, using anti-PD-L1 antibody 22C3. Staining was graded by using an immunoreactive score encompassing intensity/distribution and was divided into negative, weak, moderate, and strong. Because PD-L1 staining can be heterogeneous, we also stained 10 whole sections of sarcomatoid/desmoplastic mesotheliomas (SMMs) and 10 whole sections of spindled RMPs in the form of organizing pleuritis, the major morphologic differential of SMM. These 20 cases were not on the tissue microarrays. Results.— RMPs showed generally negative, or occasionally weak staining in either the epithelioid or spindle cell compartments, whereas moderate to strong staining was seen in 10 of 14 SMMs but only in 6 of 48 epithelioid mesotheliomas (EMMs). The difference between SMM and RMP staining was statistically significant (P < .001), but between EMM and RMP was not significant. Whole section cases of organizing pleuritis showed no staining (N = 8) or weak staining (N = 2), whereas moderate/strong staining was seen in 9 of 10 whole sections of SMMs, a statistically significant difference (P = .02). There were no “hot spots” of RMP staining, suggesting that heterogeneous staining of RMPs is not a confounder. Conclusions.— Strong diffuse PD-L1 staining using antibody 22C3 supports a diagnosis of SMM when the differential diagnosis is RMPs, but PD-L1 staining is not useful for separating EMMs from RMPs.
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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) | 0.001 | 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".