Multiclass machine learning models for molecular subtype identification of pediatric low-grade glioma using bi-institutional MRIs for precision medicine
Bibliographic record
Abstract
Pediatric Low-Grade Glioma (pLGG) is the most common pediatric brain tumor, and radiomics-based machine learning (ML) models have shown promise in identifying BRAF fusion and BRAF p.V600E mutation. This bicentric retrospective study included 495 children diagnosed between 1999 and 2023. The local hospital dataset comprised Magnetic Resonance Imaging (MRI) scans of patients with BRAF fusion (n = 190), BRAF p.V600E mutation (n = 95), FGFR1 (n = 25), and other molecular subtypes (n = 144), while an external dataset included BRAF fusion (n = 32) and BRAF p.V600E mutation (n = 9) cases. Radiomics features were extracted from Fluid-Attenuated Inversion Recovery images, and Random Forest classifiers were trained using Monte Carlo data splits and leave-one-out validation. The best-performing model achieved an average one-vs-the-rest area under receiver operating characteristic curve of 0.819 (95% confidence interval [0.791, 0.848]). This study highlights the potential of radiomics-based ML models for molecular subtype differentiation in pLGG, with per-patient predictions enabling outlier identification and subgroup performance evaluation.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".