Utility of OLIG2 immunostaining in pediatric brain tumors with embryonal morphology
Bibliographic record
Abstract
This study evaluates the diagnostic utility of OLIG2 immunohistochemistry for distinguishing between pediatric high-grade gliomas (pHGG) and embryonal tumors (ETs) of the CNS. Utilizing a retrospective pediatric cohort (1990-2021) of 56 CNS tumors, classified initially as primitive neuroectodermal tumors or CNS ET, we reclassified the cases based on WHO CNS5 criteria after comprehensive review and additional molecular testing that included next-generation sequencing and DNA methylation profiling. Our results indicate that OLIG2 immunopositivity was negative or minimal in a significant subset of pHGG cases (6 out of 11). At the same time, it showed diffuse expression in all cases of CNS neuroblastomas with FOXR2 activation (5/5), demonstrating its limited specificity in differentiating between pHGG and ET. Variable OLIG2 expression in other ETs, ATRT, and ETMR suggests the broader diagnostic implications of the marker. Furthermore, incidental findings of OLIG2 positivity in cases traditionally expected to be negative, such as medulloblastoma and ependymoma, introduce an additional layer of complexity. Together, these findings highlight the challenges of relying solely on OLIG2 immunostaining for accurate tumor classification in pediatric CNS neoplasms and underscore the importance of an integrated diagnostic approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".