UCSF

Glioma is a heterogeneous and incurable neoplastic mass derived from the glial cells in the brain. The clinical course of a glial tumor can range from slow growing to highly aggressive and it is driven by the genetic and epigenetic alterations within the neoplastic cells’ DNA. In order to diagnose both that a patient has a glioma and what kind of glioma it may be, it is necessary to acquire magnetic resonance images (MRI) of the brain. MRI not only provides unparalleled soft tissue contrast in the brain compared with other tomographic imaging techniques (e.g. CT), it is also incredibly flexible: in addition to structural anatomy of the lesion, it can probe the physiology (e.g. diffusion, perfusion) and metabolism of the brain. Together, these data guide neuroradiologists and neurooncologists to provide the optimal treatment plan for a patient with glioma. Even with the most talented clinicians and the highest-quality MR acquisitions, there still exist issues along the trajectory of diagnosing a glioma. In this dissertation, I harnessed the incredibly rich biological information within the pixels of MRI with modern advances in data science and computer vision to address three of the most urgent problems along the diagnostic pipeline: 1) Can we identify a patient’s glioma subtype prior to surgical intervention?; 2) Can we create an automatic MR dashboard for clinicians to monitor patient disease over time?; and 3) When a patient appears to recur, is it a true glioma or the effect of radiation therapy?