Keywords: Tumors (Pre-Treatment), Machine Learning/Artificial Intelligence, Fractal Dimension, Radiogenomic, Lacunarity, Glioma
Motivation: The presence of structural and geometric variations within gliomas, even among those with similar histologic grades, potentially reflect the phenotypic heterogeneity because of the distinct genetic and epigenetic landscape.
Goal(s): To develop a non-invasive radiogenomic platform to identify IDH and MGMT status using the geometry of glioma subcomponent.
Approach: Fractal dimension and Lacunarity, non-Euclidean geometric measures of glioma subcomponents, were estimated using MR images and wrapped in artificial intelligence-based models to discriminate IDH status and MGMT status.
Results: The combination of fractal dimension or lacunarity of enhancing and nonenhancing glioma subcomponent is the definitive discriminator of IDH status as wildtype or mutant.
Impact: Fractal Dimension and Lacunarity of Glioma subcomponents are unique for IDH-Mutant and IDH-Wildtype gliomas. Fractal-geometry analysis can serve as an effective non-invasive tool for identifying IDH-status prior to biopsy and surgical interventions, thereby improving the clinical management of glioma patients.
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