Medulloblastoma Molecular Subgrouping and Outcomes Data of a Single Center From a Low‐ and Middle‐Income Country
Bibliographic record
Abstract
INTRODUCTION: Medulloblastoma (MB) is the most common malignant childhood brain tumor. Molecular subgrouping of MB has become a major determinant of management in high-income countries. Subgrouping is still very limited in low- and middle-income countries (LMICs), and its relevance to management with the incorporation of risk stratification (low risk, standard risk, high risk, and very high risk) has yet to be evaluated in this setting. We describe molecular findings from a tertiary care center in Pakistan and their implications for outcome. METHODS: Children aged between 3 and 18 years diagnosed with MB from April 2014 to December 2020 at Aga Khan University Hospital (AKUH) were included. Subgrouping was performed by NanoString through a collaboration with The Hospital for Sick Children, Toronto. RESULTS: Thirty-seven patients (30 males) were included in this study; median age was 9 years. Twenty patients (54.1%) were high-risk, including 12 with metastatic disease. In 30 children, there was a clear molecular subgroup: 4 wingless (WNT) (10.8%), 6 sonic hedgehog (SHH) (16.2%), 3 Group 3 (8.1%), and 17 Group 4 (45.9%) MBs. Molecular subgrouping was inconclusive for three patients (8.1%) and not done in four patients (10.8%). All patients underwent surgery; 26 patients received radiation therapy at AKUH, and 9 were referred outside for radiotherapy; 24 patients received chemotherapy at AKUH (10 outside AKUH). Overall survival (OS) at 5 years was 100%, 66.7%, 66.7%, and 88.2% for WNT, SHH, Group 3, and Group 4 patients, respectively (p = 0.668). Low- and standard-risk patients had a 5-year OS of 100%, whereas very high-risk patients exhibited a significantly lower OS of 0% (p < 0.001). CONCLUSION: WNT and Group 4 patients had excellent results despite one WNT patient having metastatic disease and eight Group 4 patients being high risk. Our study depicts that molecular subgrouping aids in accurately predicting survival, suggesting the potential benefit of tailored testing and treatment in the LMIC setting.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".