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 YeomPublished 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.