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The first, \u201cRadiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors,\u201d , was an exploratory study that relied on 115 of our 253 patients. The second, \u201cDataset size sensitivity analysis of machine learning classifiers to differentiate molecular markers of pediatric low-grade gliomas based on MRI,\u201d Oncology and Radiotherapy 16 (S1) 2022: 01\u201306, relied on 251 of our 253 patients. These previous studies aimed to establish a relationship between radiomics features and BRAF status, and to determine the best machine learning model on this classification task.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 Retrospective\u2022 Diagnostic or prognostic study\u2022 Multicenter study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}