Guidelines for pathologic diagnosis of malignant mesothelioma: a consensus statement from the International Mesothelioma Interest Group.
Bibliographic record
Abstract
CONTEXT: Malignant mesothelioma (MM) is an uncommon tumor that can be difficult to diagnose. OBJECTIVE: To develop practical guidelines for the pathologic diagnosis of MM. DATA SOURCES: A pathology panel was convened at the International Mesothelioma Interest Group biennial meeting (October 2006). Pathologists with an interest in the field also contributed after the meeting. CONCLUSIONS: There was consensus opinion regarding (1) distinguishing benign from malignant mesothelial proliferations (both epithelioid and spindle cell lesions), (2) cytologic diagnosis of MM, (3) key histologic features of pleural and peritoneal MM, (4) use of histochemical and immunohistochemical stains in the diagnosis and differential diagnosis of MM, (5) differentiating epithelioid MM from various carcinomas (lung, breast, ovarian, and colonic adenocarcinomas and squamous cell and renal cell carcinomas), (6) diagnosis of sarcomatoid mesothelioma, (7) use of molecular markers in the differential diagnosis of MM, (8) electron microscopy in the diagnosis of MM, and (9) some caveats and pitfalls in the diagnosis of MM. Immunohistochemical panels are integral to the diagnosis of MM, but the exact makeup of panels used is dependent on the differential diagnosis and on the antibodies available in a given laboratory. Immunohistochemical panels should contain both positive and negative markers. The International Mesothelioma Interest Group recommends that markers have either sensitivity or specificity greater than 80% for the lesions in question. Interpretation of positivity generally should take into account the localization of the stain (eg, nuclear versus cytoplasmic) and the percentage of cells staining (>10% is suggested for cytoplasmic membranous markers). These guidelines are meant to be a practical reference for the pathologist.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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".