A non-transitory computer-readable storage device storing computer-executable instructions that when executed by a computer control the computer to perform a method for computing a quantified difference between an image of a region of tissue of a patient demonstrating glioblastoma multiforme pathology and an healthy template, the method comprising: accessing a diagnostic image of a region of tissue in a patient demonstrating glioblastoma multiforme pathology; generating a segmented diagnostic image by segmenting a tumor region from the diagnostic image; generating a pre-processed diagnostic image by pre-processing the segmented diagnostic image, where pre-processing the segmented diagnostic image includes: correcting bias-induced intensity inhomogeneity using an N4 bias-correction approach; intensity normalizing the segmented diagnostic image with an atlas of MRI images representing healthy brains, where the atlas of MRI images includes a healthy brain mask; skull stripping the segmented diagnostic image by non-rigidly registering the atlas of MRI images representing healthy brains with the segmented diagnostic image; generating a patient-specific brain mask based, at least in part, on the segmented diagnostic image and the healthy brain mask; generating a registered diagnostic image by registering the pre-processed diagnostic image with a healthy template, where registering the pre-processed diagnostic image includes registering the pre-processed diagnostic image with the atlas of MRI images using a diffeomorphic registration approach or a Glioma Image Segmentation and Registration (GLISTR) approach; and computing a quantified difference between the pre-processed diagnostic image and the healthy template, where computing the quantified difference between the pre-processed diagnostic image and the healthy template comprises: computing a deformation field based, at least in part, on the registered diagnostic image, where the deformation field includes a voxel-wise deformation field vector in three planar dimensions; computing a magnitude of deformation for a voxel using a Euclidean norm of scalar values of deformation orientations associated with the voxel-wise deformation field vector; and computing a mass effect deformation heterogeneity (MEDH) value for the region of tissue based, at least in part, on the magnitude of deformation. accessing a diagnostic image of a region of tissue in a patient demonstrating glioblastoma multiforme pathology; generating a segmented diagnostic image by segmenting a tumor region from the diagnostic image; generating a pre-processed diagnostic image by pre-processing the segmented diagnostic image, where pre-processing the segmented diagnostic image includes: correcting bias-induced intensity inhomogeneity using an N4 bias-correction approach; intensity normalizing the segmented diagnostic image with an atlas of MRI images representing healthy brains, where the atlas of MRI images includes a healthy brain mask; skull stripping the segmented diagnostic image by non-rigidly registering the atlas of MRI images representing healthy brains with the segmented diagnostic image; generating a patient-specific brain mask based, at least in part, on the segmented diagnostic image and the healthy brain mask; correcting bias-induced intensity inhomogeneity using an N4 bias-correction approach; intensity normalizing the segmented diagnostic image with an atlas of MRI images representing healthy brains, where the atlas of MRI images includes a healthy brain mask; skull stripping the segmented diagnostic image by non-rigidly registering the atlas of MRI images representing healthy brains with the segmented diagnostic image; generating a patient-specific brain mask based, at least in part, on the segmented diagnostic image and the healthy brain mask; generating a registered diagnostic image by registering the pre-processed diagnostic image with a healthy template, where registering the pre-processed diagnostic image includes registering the pre-processed diagnostic image with the atlas of MRI images using a diffeomorphic registration approach or a Glioma Image Segmentation and Registration (GLISTR) approach; and computing a quantified difference between the pre-processed diagnostic image and the healthy template, where computing the quantified difference between the pre-processed diagnostic image and the healthy template comprises: computing a deformation field based, at least in part, on the registered diagnostic image, where the deformation field includes a voxel-wise deformation field vector in three planar dimensions; computing a magnitude of deformation for a voxel using a Euclidean norm of scalar values of deformation orientations associated with the voxel-wise deformation field vector; and computing a mass effect deformation heterogeneity (MEDH) value for the region of tissue based, at least in part, on the magnitude of deformation. computing a deformation field based, at least in part, on the registered diagnostic image, where the deformation field includes a voxel-wise deformation field vector in three planar dimensions; computing a magnitude of deformation for a voxel using a Euclidean norm of scalar values of deformation orientations associated with the voxel-wise deformation field vector; and computing a mass effect deformation heterogeneity (MEDH) value for the region of tissue based, at least in part, on the magnitude of deformation.