Utpal Chandra DasWatit BenjapolakulManoj GuptaTimporn VitoonpongPannee SuanpangChanyanan SomthawinpongsaiSujin ButdisuwanAziz Nanthaamornphong2025-03-102025-03-102024IEMECON 2024 - 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks979-835038731-510.1109/IEMECON62401.2024.108466612-s2.0-85218093732https://repository.dusit.ac.th//handle/123456789/4446This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.Brain CancerDeep LearningImage processingMRI ImagesConference paperScopus