Radiogenomics in Neurooncology
Vast
amounts of molecular data characterizing the genome, epi-genome and
transcriptome are becoming available for a wide range of cancers. In addition,
new computational tools for quantitatively analyzing medical and pathological
images are creating new types of phenotypic data. Now we have the opportunity to integrate the
data at molecular, cellular and tissue scale to create a more comprehensive
view of key biological processes underlying cancer. Our goal is to develop
computational methods to realize multi-scale biomedical data fusion. To
accomplish this we develop computational methods to
derive quantitative image features from MR images that characterize the
radiographic phenotype of glioblastoma lesions. Based on these quantitative
image features we create radiogenomic maps by associating the radiographic
phenotype with various molecular data. These models can have profound
contributions towards predicting diagnosis and treatment. Our results show that
radiogenomic approaches studying brain tumors have
the potential to predict clinical and molecular characteristics of tumors
non-invasively. Acknowledgements
National Institute of Biomedical Imaging and Bioengineering
of the National Institutes of Health under Award Number R01EB020527. References
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