Prognosis of Glioblastoma using MRI data
Session Number
Project ID: MEDH 08
Advisor(s)
Dr. Jane Wu; Northwestern University Feinberg School of Medicine
Ryan Spear; Northwestern University Feinberg School of Medicine
Discipline
Medical and Health Sciences
Start Date
22-4-2020 8:30 AM
End Date
22-4-2020 8:45 AM
Abstract
Glioblastoma is a highly invasive malignant tumor caused by cancerous astrocytes. Due to the irregular, amorphous form, it is often difficult to identify and distinguish between the tumor core and surrounding edema in MRI imaging, making it difficult for doctors to accurately prognose the condition. This project aims to identify survival using machine learning techniques between the size and location of the tumor and the age of the patient to more accurately prognose a patient with glioblastoma. In order to do so, a segmentation program would segment the images of the tumor from the open source database BraTs 2019 provided by the University of Pennsylvania. Using the images to train the model as well as the provided information about the patients survival and age will be used as factors for a support vector machine to be able to prognose patients into short and long term survival. Data is yet to be collected.
Prognosis of Glioblastoma using MRI data
Glioblastoma is a highly invasive malignant tumor caused by cancerous astrocytes. Due to the irregular, amorphous form, it is often difficult to identify and distinguish between the tumor core and surrounding edema in MRI imaging, making it difficult for doctors to accurately prognose the condition. This project aims to identify survival using machine learning techniques between the size and location of the tumor and the age of the patient to more accurately prognose a patient with glioblastoma. In order to do so, a segmentation program would segment the images of the tumor from the open source database BraTs 2019 provided by the University of Pennsylvania. Using the images to train the model as well as the provided information about the patients survival and age will be used as factors for a support vector machine to be able to prognose patients into short and long term survival. Data is yet to be collected.