T2-FLAIR Mismatch Sign in Pediatric Low-Grade Glioma
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: No qualitative imaging feature currently predicts molecular alterations of pediatric low-grade gliomas with high sensitivity or specificity. The T2-FLAIR mismatch sign predicts IDH-mutated 1p19q noncodeleted adult gliomas with high specificity. We aimed to assess the significance of the T2-FLAIR mismatch sign in pediatric low-grade gliomas. MATERIALS AND METHODS: Pretreatment MR images acquired between January 2001 and August 2018 in pediatric patients with pediatric low-grade gliomas were retrospectively identified. Inclusion criteria were the following: 1) 0–18 years of age, 2) availability of molecular information in histopathologically confirmed cases, and 3) availability of preoperative brain MR imaging with non-motion-degraded T2-weighted and FLAIR sequences. Spinal cord tumors were excluded. RESULTS: Three hundred forty-nine patients were included (187 boys; mean age, 8.7 [SD, 4.8] years; range, 0.5–17.7 years). KIAA1549–B-Raf proto-oncogene (BRAF) fusion and BRAF p.V600E mutation were the most common molecular markers (n = 148, 42%, and n = 73, 20.7%, respectively). The T2-FLAIR mismatch sign was present in 25 patients (7.2%). Of these, 9 were dysembryoplastic neuroepithelial tumors; 8, low-grade astrocytomas; 5, diffuse astrocytomas; 1, a pilocytic astrocytoma; 1, a glioneuronal tumor; and 1, an angiocentric glioma. None of the 25 T2-FLAIR mismatch pediatric low-grade gliomas were BRAF p.V600E–mutated. Fourteen of 25 pediatric low-grade gliomas with the T2-FLAIR mismatch sign had rare molecular alterations, while the molecular subtype was unknown for 11 tumors. CONCLUSIONS: The T2-FLAIR mismatch sign was not observed in the common molecular alterations, BRAF p.V600E–mutated and KIAA1549-BRAF fused pediatric low-grade gliomas, while it was encountered in pediatric low-grade gliomas with rare pediatric molecular alterations.
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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 | 0.001 |
| 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 | 0.000 |
| 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".