LGG-32. EVALUATION OF PEDIATRIC GLIOMA OUTCOME USING INTRAOPERATIVE MRI: A COHORT STUDY USING I-MiND (IMRIS MULTICENTER iMRI NEUROSURGERY DATABASE)
Bibliographic record
Abstract
Gliomas in pediatric patients remain challenging to treat. Intraoperative MRI (iMRI) may be a method to improve resection volumes, avoid critical structures, and guide intraoperative therapy in real-time. We analyzed pediatric patients (age ≤18 years) in the I-MiND (IMRIS Multicenter iMRI Neurosurgery Database) who underwent resection of pathology-confirmed gliomas. A total of 327 patients (mean age 9.7 ± 4.6, 56.3% male) were identified who underwent treatment (13 neurosurgeons; 5 academic centers). Most tumors were World Health Organization (WHO) grade I (63.3%) without prior resection (82.6%) and were 31.7 ± 21.4 mm in average largest dimension. Of the 300 patients that underwent iMRI, additional tumor was resected from 140 patients (42.8%). Of the 37 patients with specimen sent to pathology after iMRI, 33 show positive tumor pathology (89.2%). The average surgery and room times were 5.6 ± 2.1 and 7.6 ± 2.2 hours, respectively. Mean overall survival (OS) for WHO grade I, II, III, and IV tumors was 35.4 ± 31.2, 20.1 ± 18.6, 19.2 ± 11.4, and 10.1 ± 10.6 months, respectively. On survival analysis, WHO grade and extent of resection impacted both OS and progression free survival (p<0.05). For patients with WHO grade I, II, III and IV tumors, a gross total resection rate of 73.9%, 75.0%, 72.2%, and 37.5% was observed, respectively. We evaluated the largest multicenter study of pediatric gliomas treated using iMRI. Increased extent of resection improved OS and gross-total resection was achievable in the majority of patients. The majority of post-iMRI resected specimens included tumors. Continued refinement of iMRI-techniques in pediatric patients may help improve outcomes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".