DIPG-67. RE-IRRADIATION PRACTICES AND OUTCOMES IN PATIENTS WITH DIPG/DMG: A REPORT FROM THE INTERNATIONAL DIPG REGISTRY
Bibliographic record
Abstract
Abstract BACKGROUND Given that radiation therapy is the only treatment modality demonstrated to result in any clinical benefit for children with DIPG, re-irradiation therapy has been explored as a treatment option for progressive DIPG. Several studies suggest re-irradiation is feasible, and may lengthen survival for children with progressive DIPG. However, any re-irradiation benefits are unclear and no standard of care (dose, fractionation, volume, timing, clinical status) has been defined. The aims of this study are to evaluate re-radiation therapy practices for children with progressive DIPG/DMG and to define a historical cohort of children with DIPG/DMG who have received re-radiation (re-XRT) for progressive DIPG/DMG. METHODS Data was extracted from the International DIPG Registry, and analyzed with descriptive statistics. RESULTS Of 1214 patients in the iDIPG Registry, 113 receiving re-XRT. Patients were diagnosed between 2002-2022, with the majority of patients diagnosed over the last decade. Of those 113 patients with specified data, at re-irradiation, n=68 (60%) received photon and n= 4 (4%) were treated with proton radiation. The median dose at re-XRT was 25 Gy (20-30 IQR) in 10 fractions (10-14 IQR). Time between initial XRT and re-XRT was 41.5 weeks (29.5-54 weeks). OS from diagnosis was 18 months (range, 15-23 mo); OS from re-XRT was 6 months (range, 4-10 mo). CONCLUSIONS Re-radiation therapy for children with DIPG/DMG is becoming a more common practice and appears to have OS benefit, with post-progression OS of 6 mo comparing favorably to historical data of 2.3 mo. [Cooney T et al, Neuro Oncol 2017] Efforts are underway to define patient selection and tolerability. Data extraction from the SIOPE DIPG/DMG Registry is also ongoing; data from both registries will be collated and uniformly presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".