MB-11 * IMPACT OF MOLECULAR SUB-TYPE AND CRANIOSPINAL IRRADIATION (CSI) DOSE ON RELAPSE OF MEDULLOBLASTOMA
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of craniospinal irradiation (CSI) dose and molecular sub-type on medulloblastoma relapse rates. METHODS: 115 medulloblastoma patients were treated with 18- ≥ 36 Gy CSI. Molecular subtyping data was done by nanostring assay [WNT, SHH, and Group 3/4 non-WNT/non-SHH (NWNS)]. A CSI dose schema based on prognostic implications of molecular subtype was devised: ≥36 Gy for M+ or Group 3; ≤18 Gy for non-disseminated WNT; 23.4 Gy all others. Relapse rates were assessed and correlated with CSI dose. RESULTS: After excluding patients with incomplete data, the final cohort included 36 subjects with median age 8.7 years (range 3.3–16.9) and median follow-up 4.1 years (0.2-3.7). Molecular subtypes were as follows: 2-WNT, 6-SHH, 28-NWNS. CSI dose was 18 Gy(11 cases), 23.4 Gy(9), and ≥36 Gy(16). CSI dose was divergent from the proposed schema in 18 cases (14-lower, 4-higher). There were 14 relapses (by molecular subtype: 0-WNT, 1-SHH, 13-NWNS; by histology: 8-classic, 5-LC/A, 1-EN), with median time to relapse 18.5 months (0.43-62). Seven of 14 tumors treated to a lower dose relapsed (1 SHH; 6 NWNS); only 1 (NWNS) of 4 patients with higher divergent dose relapsed. Six of 18 patients treated to similar dose relapsed; all were NWNS, and 4/6 had M3 disease. Eleven of 14 patients who relapsed died. CONCLUSION: Molecular subtyping may help avoid under-treating some NWNS tumors but innovative strategies for metastatic NWNS are needed. We confirm that histological subtypes may hold some predictive value as 5/7 LC/A relapsed and no desmoplastic tumors relapsed. In this small sample, no WNT relapse occurred and only 1 SHH relapsed, suggesting that dose de-escalation can be cautiously explored for these subtypes. Future research directions include applying an MRI image-based algorithm to subtype the entire cohort, and combining molecular data with other institutions to examine relapse patterns by subtype.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".