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Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

Michael ZhangSamuel W WongJason N WrightSebastian M ToescuMaryam MohammadzadehMichelle HanSeth LummusMatthias W WagnerDerek W YeciesHollie LaiAzam EghbalAlireza RadmaneshJordan NemelkaStephen HarwardMichael MalinzakSuzanne LaughlinSebastien PerreaultKristina R M BraunArastoo VossoughTina PoussaintRobert GoettiBirgit Ertl-WagnerChang Y HoOzgur OztekinVijay RamsawamiKshitij MankadNicholas A VitanzaSamuel H CheshierMourad SaidKristian AquilinaEric ThompsonAlok JajuGerald A GrantRobert M LoberKristen W Yeom
Published in: Neurosurgery (2021)
An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.
Keyphrases
  • machine learning
  • high resolution
  • magnetic resonance imaging
  • deep learning
  • contrast enhanced
  • artificial intelligence
  • computed tomography
  • big data
  • magnetic resonance
  • mass spectrometry
  • photodynamic therapy