Novel Immunohistochemical Markers in the Diagnosis of Nonglial Tumors of Nervous System
Bibliographic record
Abstract
Immunohistochemistry (IHC) has become an important adjunct tool in diagnostic neuro-oncology practice enabling immunophenotypic characterization of tumor cells. There have been several recent publications regarding new IHC markers that are useful for diagnosis of brain tumors. To introduce the latest advances in IHC in this field, we review the features of novel IHC marker antibodies applicable to selected nonglial tumors in the nervous system, based on recently published reports and our own experiences. We discuss (1) aquaporin-1 and alpha-inhibin for hemangioblastoma, (2) beta-catenin for craniopharyngioma, (3) brachyury for chordoma, and (4) INI-1 for hereditary schwannomas. All the markers presented here are used primarily for supporting or confirming the histologic diagnosis, with the exception of (4), which may be of help in identification of inherited forms in schwannomas. As with other surgical pathology practices, the judicious use of a panel of IHC antibodies selected on the basis of the histologic findings is important for an accurate diagnosis of brain tumors. Of note is that IHC results should be always interpreted in the histopathologic context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".