MB-59IMAGING OF METASTATIC MEDULLOBLASTOMA IN THE MOLECULAR ERA
Bibliographic record
Abstract
BACKGROUND: Recently it has been shown that the four principle subgroups of medulloblastoma can be discerned using conventional MRI. Herein we aim to provide robust neuroimaging correlates of medulloblastoma metastases in a subgroup dependent manner. METHODS: We identified 35 metastatic cases of medulloblastoma (out of 154-22.7%). Samples were subgrouped using limited gene expression profiling and/or genome wide methylation arrays. Diagnostic MRI scans were blindly reviewed and correlated with molecular subgroup. RESULTS: Molecular subgrouping was available in 29 metastatic cases (Wnt = 0, SHH = 5, Group 3 = 11, Group 4 = 13). In SHH two cases had multi-lobulated lesions previously interpreted as metastatic disease. Although Group 4 tumors frequently do not enhance, the enhancement pattern of metastases is heterogeneous and metastases frequently enhance, although more apparent with diffusion weighted imaging. Interestingly metastatic Group 3 patients had significantly smaller primary tumor at the time of diagnosis. Metastatic spread to the suprasellar region was observed in 8 patients (22.8% of all metastatic) and were mainly restricted to Group 4 (n = 6) and Group 3 (n = 2). Three patients with solitary suprasellar metastases were Group 4. No visual or endocrine impairment was observed in these cases. CONCLUSIONS: The imaging characteristics of medulloblastoma metastases are highly subgroup specific. Group 4 tumors can present with non-enhancing metastases more obviously detected by diffusion weighted imaging and have a strong predilection for the suprasellar region. Clinical trial consortiums need to take into account the unique pattern of Group 4 metastases when evaluating children with metastatic medulloblastoma.
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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) | 0.001 | 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".