4. “Biphasic” histology is associated with the non-WNT/SHH molecular subtype of medulloblastoma
Bibliographic record
Abstract
Introduction In 2007, Ellison et al coined the term “biphasic” medulloblastoma (B-MB) to characterize histology that mimicked the desmoplastic nodular (DN) variant on routine staining, but which lacked internodular reticulin deposition. Via interphase FISH, and utilizing markers for 9q22 and chromosome 17 alterations (ie, -17p and i17q), Ellison et al. suggested that B-MB and DN-MB were genetically different. Methods We performed a clinicopathologic review of MBs treated at BCCH from 1986-2011. Using nanoString’s n Counter Analysis System (nCAS), each tumor was molecularly subtyped (ie, WNT, SHH, group 3 or group 4). All original glass slides were reviewed to determine WHO histologic subtype [ie, classic, large cell anaplastic (LCA), DN, MB with extensive nodularity (MBEN)]. Tumors were also evaluated for nodularity (scattered vs. frequent) and advanced neuronal differentiation. Reticulin staining was assessed on all cases. Results 20 B-MB were identified; by WHO definition, most of these resided within the classic category (N=19), while one was LCA. 13 of 20 B-MB displayed ‘scattered” nodules; by molecular subtype, these included eight group 4, four group 3 and one WNT tumors. Seven of the 20 B-MB exhibited “frequent” nodules; by molecular subtype, these included six group 4 and one group 3 tumors. Statistical analysis confirmed this non random distribution of B-MB across molecular subtypes. Conclusion Our data confirm the work of Ellison et al. that suggested B-MB is genetically different than DN-MB. In particular, B-MB resides in the non-WNT/SHH molecular category, but especially amongst group 4 when nodularity is “frequent”.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".