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A radiomics approach to distinguish non-contrast enhancing tumor from vasogenic edema on multi-parametric pre-treatment MRI scans for glioblastoma tumors.

, , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)

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Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study., , , , , , , , , and 2 other author(s). CoRR, (2020)STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI., , , , , , , and . MICCAI (4), volume 11767 of Lecture Notes in Computer Science, page 611-619. Springer, (2019)Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus controls., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)A radiomics approach to distinguish non-contrast enhancing tumor from vasogenic edema on multi-parametric pre-treatment MRI scans for glioblastoma tumors., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma., , , , , , , , , and 3 other author(s). IEEE Trans. Medical Imaging, 41 (7): 1764-1777 (2022)Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma? - A Feasibility Study., , , , , , , , , and 2 other author(s). Frontiers Comput. Neurosci., (2020)RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment., , , , , , , and . IEEE J. Biomed. Health Informatics, 26 (6): 2627-2636 (2022)Spatial-And-Context Aware (SpACe) "Virtual Biopsy" Radiogenomic Maps to Target Tumor Mutational Status on Structural MRI., , , , , , , , , and 1 other author(s). MICCAI (2), volume 12262 of Lecture Notes in Computer Science, page 305-314. Springer, (2020)Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI., , , , , , , , , and 3 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 10950 of SPIE Proceedings, page 109501E. SPIE, (2019)Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma., , , , , , , , , and 3 other author(s). CoRR, (2021)