UCSF

Gliomas are highly infiltrative, heterogenous brain tumors with poorly defined margin, and varying overall survival based on molecular subtype and grade. Despite recent developments in new diagnostic and treatment tools for gliomas, progression free survival and overall survival has only improved marginally for patients with glioma. Furthermore, treatment of glioma tends to be “one size fits all”, which can lead to either undertreating or overtreating the subclinical disease. Thus, the management of gliomas needs to be more patient-specific and more flexible over the course of the disease if the goal is to maximize both the longevity and quality of life of these patients.Recent advancement in MRI and radiation therapy research has opened the door for many opportunities to answer these questions. While the use of MRI in the clinic has been mostly limited to anatomical imaging, other MRI modalities have been gaining a lot of traction and have been proven to be able to provide clinical information not available in anatomical MRI. However, incorporating multimodal MRI in glioma management is a difficult task, because more advanced MRI acquisitions are not consistently acquired across institutions, and effectively understanding the consequences of changes observed on multimodal MRI over time is difficult even for trained radiologist. Artificial Intelligence has shown promise in making predictions from multi-parametric images, as multiple inputs can be given at the same time, and all processing and prediction tasks can be pre-trained and automatic applied. In this dissertation, we attempted to use multimodal MRI and artificial intelligence to improve both the diagnosis and treatment planning for patients with glioma. First, we developed an 1D deep learning based model that can help predict tumor histopathology noninvasively using the full spectrum of 1H MR Spectroscopic Imaging data. Then, we developed 3D segmentation-based deep learning model using multi-parametric MRI to redefine the clinical target volume for radiotherapy treatment planning and found that our model performed better than current practice, both in terms of better detecting subclinical disease and future progression, as well as sparing normal brain tissue. Finally, we highlighted the efficacy of using multi-parametric MRI in predicting a patient’s progression free and overall survival and improved the model performance by applying different types of masks.