IMP3 and GLUT-1 Immunohistochemistry for Distinguishing Benign From Malignant Mesothelial Proliferations
Bibliographic record
Abstract
Distinguishing malignant mesotheliomas from benign mesothelial proliferations on hematoxylin and eosin-stained sections can be extremely challenging. Various immunohistochemical stains have been suggested to help in making this distinction, but all are controversial. Recently, IMP3 (insulin-like growth factor II mRNA binding protein 3) and GLUT-1 (glucose transporter protein 1) have been proposed as immunohistochemical markers that are positive in mesotheliomas but not in benign proliferations. We evaluated the performance of these markers on a tissue microarray containing 30 malignant mesotheliomas and 48 benign thoracic or abdominal mesothelial proliferations. IMP3 was positive in 53% of malignant and 27% of benign cases (P=0.03), whereas GLUT-1 was positive in 60% of malignant and 13% of benign cases (P=0.0003). Forty-three percent of malignant cases, but only 4% of benign cases, were positive for both IMP3 and GLUT-1 (P=0.00003). We conclude that, statistically, both IMP3 and GLUT-1 are more frequently positive in malignant compared with benign mesothelial processes; however, the frequency of positive staining in benign cases is too high to allow their diagnostic use as single stains. The combination of both markers may be of greater diagnostic value, but this hypothesis should be confirmed in further studies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".