Abstract
Glioblastoma is the most common primary brain cancer worldwide. Novel treatment strategies are urgently needed since glioblastoma is nearly universally fatal with a median overall survival of 18 months. A major challenge is predicting how patients will respond to first-line treatment, a combination of surgery, chemotherapy, and radiation therapy. Approximately half of patients will have what looks to be tumor growth on their post-treatment MRI, termed progression. Although roughly half of patients with progression will turn out to have pseudoprogression, which is believed to be edema and inflammation caused by the immune system’s reaction to treatment and represents a good response to therapy. In fact, patients with pseudoprogression tend to do better than the general glioblastoma population and have a median overall survival of up to 3 years. On the other hand, patients with true progression of disease (tumor growth/poor response to therapy) tend to do worse than the general glioblastoma population and have a medial overall survival of 10 months. The current gold-standard to distinguish between true and pseudoprogression is to “watch and wait” – continue monitoring with serial imaging for 4-6 months after treatment ends and see if the patient clinically worsens (true progression) or stabilizes (pseudoprogression). My thesis project encompasses: 1) analyzing daily MRI data from a novel MRI-guided radiation therapy device to describe the kinetics of glioblastoma tumors during treatment; 2) utilizing machine learning for auto-contouring these tumors on daily MRI, which allows for automatic detection of changing tumor anatomy and early identification of progression; and 3) analysis of the tumor contours to choose imaging markers that are prognostic for true versus pseudoprogression. In an initial cohort of glioblastoma patients treated with MRI-guided radiation therapy, I found imaging markers that are 90% accurate at predicting between patients with true and pseudoprogression at week 1 of treatment, 5-7 months earlier than the gold-standard method.