Abstract: Embodiments discussed herein facilitate determination of tumor mutation status based on context and spatial information. One example embodiment can access a MRI scan of a tumor comprising voxels; extract radiomic feature(s) from the voxels; generate a spatial feature descriptor indicating probabilities the tumor has a first mutation status and a second mutation status, based on the MRI scan, a first population atlas for the first mutation status, and a second population atlas for the second mutation status; provide the radiomic feature(s) and the spatial feature descriptor to a machine learning model; and receive, via the machine learning model, a map indicating, for each voxel of the voxels, a probability of the first mutation status for that voxel and a probability of the second mutation status for that voxel, wherein the map is based at least on the one or more radiomic features and the spatial feature descriptor.

Abstract: Embodiments discussed herein facilitate determination of tumor mutation status based on context and spatial information. One example embodiment can access a MRI scan of a tumor comprising voxels; extract radiomic feature(s) from the voxels; generate a spatial feature descriptor indicating probabilities the tumor has a first mutation status and a second mutation status, based on the MRI scan, a first population atlas for the first mutation status, and a second population atlas for the second mutation status; provide the radiomic feature(s) and the spatial feature descriptor to a machine learning model; and receive, via the machine learning model, a map indicating, for each voxel of the voxels, a probability of the first mutation status for that voxel and a probability of the second mutation status for that voxel, wherein the map is based at least on the one or more radiomic features and the spatial feature descriptor.

Abstract: Embodiments discussed herein facilitate generating a quantitative population atlas of tumor progression (TP) versus pseudo-progression (PsP) in Glioblastoma (GBM). A first set of embodiments discussed herein relates to generating a quantitative population atlas of TP versus PsP based on a plurality of multi-parametric (mpMRI) studies of a population of patients demonstrating GBM. A second set of embodiments discussed herein relates to computing a probability that a patient will experience PsP or TP based on a DICE analysis of a mapping of a diagnostic mpMRI study associated with the patient into the quantitative population atlas space.

Abstract: Embodiments discussed herein facilitate generating a quantitative population atlas of tumor progression (TP) versus pseudo-progression (PsP) in Glioblastoma (GBM). A first set of embodiments discussed herein relates to generating a quantitative population atlas of TP versus PsP based on a plurality of multi-parametric (mpMRI) studies of a population of patients demonstrating GBM. A second set of embodiments discussed herein relates to computing a probability that a patient will experience PsP or TP based on a DICE analysis of a mapping of a diagnostic mpMRI study associated with the patient into the quantitative population atlas space.