Multiclass Radiomics-Based Prediction of BRAF Mutation Status in Pediatric Low-Grade Gliomas Using Multisequence MRI.
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BACKGROUND AND PURPOSE: Pediatric low-grade gliomas (pLGGs) are the most common brain tumors in children and frequently harbor BRAF alterations, most commonly KIAA1549-BRAF fusions and BRAF V600E mutations, which have distinct therapeutic implications. The aim of our study was to assess multiclass radiomics-based prediction of BRAF mutation status in children with pLGG using multi-sequence MRI. This study follows TRIPOD-AI guidelines. MATERIALS AND METHODS: This retrospective bi-institutional study included pediatric patients with pLGG and known BRAF mutation status who underwent pre-surgical MRI between January 2009 and January 2023. Tumors were manually segmented, and radiomics features were extracted using PyRadiomics. Random Forest classifiers were trained for three-class classification (BRAF fusion vs BRAF V600E vs non-BRAF) using clinical-only, radiomics-only, and combined models. Performance was evaluated with leave-one-out cross-validation, and results were compared across single-sequence and multisequence approaches. Single-sequence models were trained using all available patients for each MRI sequence, whereas multisequence models were restricted to the subset of 180 patients with all four sequences available. RESULTS: 511 children were included (mean age 8.5 ± 5.1 years; 45% female). Molecular subtypes included BRAF fusion (223/511, 44.6%), BRAF V600E (105/511, 21.0%), and non-BRAF tumors (172/511, 34.4%). FLAIR sequences were available for 495, T2WI for 454, contrast-enhanced T1WI (CE-T1WI) for 285 and ADC maps for 252 children. All sequences were available for 180 children. FLAIR was the best-performing single sequence (AUC 0.82), followed by T2WI (0.80), ADC (0.77), and CE-T1WI (0.75). Reported AUC values represent macro-average one-vs-rest performance across the three molecular classes. Combined clinical-radiomics models consistently outperformed single-source models. In the 180-patient multisequence cohort, radiomics feature concatenation (macro-AUC 0.79) and ensemble modeling (0.79) both outperformed single-sequence approaches (p < 0.001). Feature analysis showed FLAIR-derived features dominated, but adding T2, ADC, and CE-T1WI improved balanced classification across subtypes. CONCLUSION: MRI-based machine learning models may support noninvasive prediction of BRAF mutation status in pLGG. FLAIR is the best-predicting single sequence, but multisequence integration was associated with improved and more balanced performance. These findings support multisequence radiomics as a promising tool to guide precision treatment in pLGG, particularly when tissue sampling is not feasible.