Machine learning-driven molecular classification of pediatric brain tumors

Brain tumors are the leading cause of cancer-related death in childhood and are generally categorized as low-grade or high-grade. More granularly however, there can be over 100 types of brain tumors which can vary widely in both prognosis and treatment. Machine learning has increasingly been applied to classify brain tumors and other cancer types but despite the success of these algorithms in classifying adult brain tumors, performance for pediatric tumors has been suboptimal in part due to the lack of sufficient training data. With larger sample sizes now available and extensive archives of in-house pediatric molecular and clinical data, the aim is to develop methods to better classify and diagnose these pediatric brain tumors, utilizing new computational techniques to analyze and integrate diverse biological datasets. By having a better understanding of what drives these tumors and more refined classification frameworks, improvements can be made to the process of matching patients with the most effective therapeutic options.

Faculty Supervisor:

Alan M. Moses

Student:

Partner:

The Hospital for Sick Children

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Public administration

University:

University of Toronto

Program: