DIPG-23. BRAINSTEM RADIATION EXPOSURE CONFERS SUBSTANTIAL RISK OF DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG) IN MEDULLOBLASTOMA SURVIVORS: A REPORT FROM THE INTERNATIONAL DIPG REGISTRY
Bibliographic record
Abstract
With improved survivorship in medulloblastoma, there has been increasing recognition of the occurrence of secondary malignant brain tumors. To date, no studies have specifically addressed the risk of diffuse intrinsic pontine glioma (DIPG) in medulloblastoma survivors. We queried the International DIPG Registry and identified six cases of DIPG with prior medulloblastoma. Six additional cases were identified in reports from recent cooperative group medulloblastoma trials. Incidence of DIPG after medulloblastoma ranged from 0.3–3.9%. All 12 cases underwent surgical resection followed by craniospinal photon irradiation (range 18–36 Gy) and posterior fossa boost (range 19.8–36 Gy). Posterior fossa exposure was greater than 53 Gy in all cases. Median time to diagnosis of secondary DIPG was 7 years (range 2–11 years). Patients died of secondary DIPG a median of 8 months after diagnosis (range 4–17 months). Molecular subgroup of primary medulloblastomas with available tissue (n=5) revealed only non-WNT, non-SHH subgroups (group 3 or 4). Tumor/germline exome sequencing of three secondary DIPGs demonstrated tumors to be H3.3 wildtype and harbor higher mutational burden than radiation-naïve DIPGs. Mutational signature analysis of secondary DIPGs showed mutations consistent with radiation-induced DNA damage (e.g., insertional event in TP53), as well as mutations in other oncogenic drivers (e.g., NRAS, PI3KCA), suggestive of distinct mutational processes compared with primary DIPGs. In conclusion, we report for the first time that survivors of pediatric medulloblastoma are at risk for the development of secondary DIPG, likely consequent to radiation exposure. This risk highlights the importance of radiation field, volume, and modality in medulloblastoma treatment.
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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 | 0.001 |
| 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".