Discriminating Glioblastoma from Normal Brain Tissue In Vivo Using Optical Coherence Tomography and Angiography: A Texture and Microvascular Analysis Approach

Abstract

Brain tumors arise from abnormal cell growth in the brain. Glioblastoma, the most common and aggressive type, poses significant challenges for identification during surgery. The primary goal of this study is to identify and differentiate normal brain tissue from glioblastoma tissue using optical coherence tomography (OCT) and OCT angiography (OCTA). These techniques offer a non-invasive way to analyze the morphological and microvascular alternations associated with glioblastoma in an animal model. To monitor the changes in morphology and vascular distribution of brain tissue as glioblastoma tumors grow, time-series OCT and OCTA results were collected for comparison. Texture analysis of OCT images was proposed using the gray-level co-occurrence matrix (GLCM), from which homogeneity and variance were calculated as discriminative parameters. Additionally, OCTA was used to assess microvascular characteristics, including vessel diameter, density, and fractal dimension. The findings demonstrate that the proposed methods can effectively distinguish between normal and cancerous brain tissue in vivo.