UCLA

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor. Magnetic resonance (MR) imaging plays an important role in the detection of brain tumors and treatment response assessment of drugs in clinical trials. Diffusion weighted magnetic resonance imaging (DW-MRI) has the potential to work as surrogate biomarker to reveal early changes in the tumor microenvironment that precede morphologic tumor changes. In this dissertation, we developed a computer-aided therapeutic response system (CADrx) for GBM brain tumors using T1w post-contrast MR and diffusion-weighted (DW) MR images in clinical trials. There are two components: 1) semi-automated segmentation of GBM brain tumors on T1w post-contrast MR images; 2) prediction of volumetric treatment response using early ADC values derived from the DW-MRI. The first component is the main focus of the dissertation. The overall goal is to first facilitate radiologists in the time-consuming task of tumor contouring and generate as reproducible segmentation as possible, and then collect potential features automatically and use machine learning techniques to explore patterns in the large-scale dataset. By doing this, we aim to provide radiologists a second opinion in tumor contouring and therapy response classification.