Aug 15, 2021

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

Neurosurgery

Michael Zhang, Samuel W Wong, Jason N Wright, Sebastian Toescu, Maryam Mohammadzadeh, Michelle Han, Seth Lummus, Matthias W Wagner, Derek Yecies, Hollie Lai, Azam Eghbal, Alireza Radmanesh, Jordan Nemelka, Stephen Harward, Michael Malinzak, Suzanne Laughlin, Sebastien Perreault, Kristina R M Braun, Arastoo Vossough, Tina Poussaint, Robert Goetti, Birgit Ertl-Wagner, Chang Y Ho, Ozgur Oztekin, Vijay Ramaswamy, Kshitij Mankad, Nicholas A Vitanza, Samuel H Cheshier, Mourad Said, Kristian Aquilina, Eric Thompson, Alok Jaju, Gerald A Grant, Robert M Lober, Kristen W Yeom

CONCLUSION: 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.

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

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