Sox10 is Superior to S100 in the Diagnosis of Meningioma
Bibliographic record
Abstract
Meningiomas are the most common primary cranial tumor arising in the central nervous system and its coverings, constituting 35.5% of primary brain tumors. Schwannomas account for 8.3% of primary neoplasms of the central nervous system. Occasionally these tumors can show overlapping morphology, most conspicuously in the fibrous variant of meningioma. In such cases, immunohistochemistry can help to establish a definitive diagnosis. Currently S100 is the most commonly used immunohistochemical stain to show neural crest differentiation in tumors. This may lead to potential misclassification of meningeal tumors, as up to 70% of fibrous meningiomas can show S100 expression. Our study sought to determine if Sox10 would prove a more specific alternative to S100 in cases of meningioma when the differential diagnosis includes schwannoma. We compared the mRNA expression of S100B and Sox10 using the publicly available GSE16581 meningioma dataset. We then studied Sox10 and S100 protein expression using immunohistochemistry in 147 cases of meningioma using tissue microarrays (TMA) and 19 cases of fibrous meningioma using full cross-sections (FCS). Sox10 and S100B mRNA expression in GSE16581 showed no significant correlation in meningothelial tumors (r=-0.002, P=0.989). By immunohistochemistry, S100 was positive in 14/19 (73.7%) of FCSs and 71/147 (48.3%) of TMA tumors, whereas Sox10 (protein name) was positive in only 1/19 (5.3%) of FCSs and 3/147 (2.0%) of TMA tumors. In summary, Sox10 is superior to S100 in the differential diagnosis of schwannoma and meningioma.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".