Gliomas, which originate from glial cells, are considered the most aggressive type of brain tumors. Currently, glioma research efforts are focused mainly on high-grade gliomas. This project aims to analyze lower-grade gliomas (LGG) in MRI and extend the understanding of LGGs. LGG segmentation, which outlines tumors in MRIs, is crucial to developing effective treatment plans. However, segmentation performed manually by radiologists is tedious, time-consuming, and often leads to inter-observer variability. Another unexplored area of LGG research is genomic subtypes. These subtypes can play a large factor in how LGGs can be treated, however there is currently no noninvasive method of identifying these subtypes. Recent studies suggest that LGG shape features have a correlation with genomic subtypes and should be investigated as a viable factor in LGG treatment options. This presents a need for additional research as most LGGs eventually develop into high-grade gliomas. The specific aims of this project include analyzing LGGs through deep learning-based segmentation, shape feature extraction, and statistical analysis to identify correlation between selected shape features and genomic subtypes. To realize these goals, programs were written and run using a publicly available LGG dataset. In terms of automatic segmentation, two models were created using different convolutional neural networks (CNN). The highest performing model used U-Net with a ResNeXt-50 backbone and yielded a 91.4% accuracy in terms of mean intersection over union (IoU) after testing. Shape feature extraction included three geometric features and 4 radiomic features which quantified tumor shape in 2D and 3D. Angular Standard Deviation (ASD), Margin Fluctuation (MF), Bounding Ellipsoid Volume Ratio (BEVR), Elongation, Major and Minor Axis Length, and Volume were tested for correlation with genomic subtype data using 49 Pearson chi-squared tests. P-values less than or equal to 0.05 suggested correlation. Statistical analysis found 16 statistically significant associations. The strongest associations were between MF and the RNASeq cluster (p < 0.00003), ASD and the RNASeq Cluster (p < 0.0005), and volume and the RPPA cluster (p < 0.0035).