MR Imaging of Pediatric Low-Grade Gliomas: Pretherapeutic Differentiation of<i>BRAF</i>V600E Mutation,<i>BRAF</i>Fusion, and Wild-Type Tumors in Patients without Neurofibromatosis-1
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The prognosis and treatment of pediatric low-grade gliomas is influenced by their molecular subtype. MR imaging remains the mainstay for initial work-up and surgical planning. We aimed to determine the relationship between imaging patterns and molecular subtypes of pediatric low-grade gliomas. MATERIALS AND METHODS: This was a retrospective bi-institutional study for patients diagnosed from 2004 to 2021 with pathologically confirmed pediatric low-grade gliomas molecularly defined as BRAF fusion, BRAF V600E mutant, or wild-type (which is neither BRAF V600E mutant nor BRAF fusion). Two neuroradiologists, blinded, independently reviewed imaging parameters from diagnostic MRIs, and discrepancies were resolved by consensus. Bivariate analysis was used followed by pair-wise comparison of the Dwass-Steel-Critchlow-Fligner method to compare the 3 molecular subtypes. Interreader agreement was assessed using κ. RESULTS: We included 70 patients: 30 BRAF fusion, 19 BRAF V600E mutant, and 21 wild-type. There was substantial agreement between the readers for overall imaging variables (κ = 0.75). BRAF fusion tumors compared with BRAF V600E and wild-type tumors were larger (P = .0022), and had a greater mass effect (P = .0053), increased frequency of hydrocephalus (P = .0002), and diffuse enhancement (p <.0001). BRAF V600E mutant tumors were more often hemispheric (P < .0001), appeared more infiltrative (P = .0002), and, though infrequent, were the only group demonstrating diffusion restriction (qualitatively; P = .0042) with a lower ADC ratio (quantitatively) (P = .003). CONCLUSIONS: BRAF fusion and BRAF V600E mutant pediatric low-grade gliomas have unique imaging features that can be used to differentiate them from each other and wild-type pediatric low-grade glioma using a standard radiology review with high interreader agreement. In the era of targeted therapy, these features can be useful for therapeutic planning before surgery.
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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 | 0.002 |
| 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 | 0.000 | 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".