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<p>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 (<i>n</i> = 190), BRAF p.V600E mutation (<i>n</i> = 95), FGFR1 (<i>n</i> = 25), and other molecular subtypes (<i>n</i> = 144), while an external dataset included BRAF fusion (<i>n</i> = 32) and BRAF p.V600E mutation (<i>n</i> = 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.</p>

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Multiclass machine learning models for molecular subtype identification of pediatric low-grade glioma using bi-institutional MRIs for precision medicine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Khashayar Namdar,
  • Matthias W. Wagner,
  • Min Sheng,
  • Peter Dirks,
  • Kristen W. Yeom,
  • Cynthia Hawkins,
  • Uri Tabori,
  • Birgit B. Ertl-Wagner,
  • Farzad Khalvati

摘要

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.