Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
Gliomas are the most common primary brain tumors in adults; most are high-grade and have a
high level of mortality. The standard treatment is to kill or remove the cancer cells. Of
course, this can only work if the surgeon or radiologist can find these cells.
Unfortunately, there are inevitably so-called "occult" cancer cells, which are not found
even by today's sophisticated imaging techniques.
This proposal proposes a technology to predict the locations of these occult cells, by
learning the growth patterns exhibited by gliomas in previous patients. We will also
develop software tools that help both practitioners and researchers find gliomas similar to
a current one, and that can autonomously find the tumor region within a brain image, which
can save radiologists time, and perhaps help during surgery.
Endpoint Classification: Efficacy Study, Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Diagnostic
image glioma patients with advanced imaging techniques to help us better characterize gliomas in the future
Eligible patients will be given the opportunity to undergo additional diagnostic imaging. These images will be anonymized and databased. the data will be analyzed using machine learning techniques.
Pretreatment, 1 month post treatment and 7 months post treatment
Albert Murtha, MD, FRCPC
Alberta Health Services
Canada: Health Canada
CNS-9-0032 / 22151-22523