Constructing imaging signatures for various diseases and disorders serving as personalized biomarkers for precision diagnosis & predictive modeling. The Artificial Intelligence in Biomedical Imaging Laboratory's long standing record on use of machine learning in neuroimaging includes studies of aging, Alzheimer’s Disease, schizophrenia, brain cancer, and functional connectivity. Some of the current challenges and targets include dissecting disease heterogeneity using semi-supervised learning methods, establishing radiogenomic markers of genetic mutations, and relating data from radiology and pathology.
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3. Davatzikos C, Rathore S. et al. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging. 2018.
4. Binder ZA, Thorne AH, Bakas S, et al. Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development. Cancer Cell. 2018.
5. Akbari H, Bakas S, et al. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol. 2018.
6. Li H, Satterthwaite TD, Fan Y. Large-scale sparse functional networks from resting state fMRI. NeuroImage. 2017.
7. Sotiras A, Toledo JB, Gur RE, Gur RC, Satterthwaite TD, and Davatzikos C. Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. PNAS. 2017.
8. Dong A, Toledo JB, Honnorat N, Doshi J, Varol E, Sotiras A, Wolk D, Trojanowski JQ, Davatzikos C. Alzheimer’s Disease Neuroimaging Initiative, “Heterogeneity of neuroanatomical patterns in prodromal Alzheimer's disease: links to cognition, progression and biomarkers”, Brain, 140(3), 735-747, March 2017.
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