| Literature DB >> 29551544 |
Alexander Lam1, Kevin Bui2, Eduardo Hernandez Rangel2, Michael Nguyentat2, Dayantha Fernando2, Kari Nelson2, Nadine Abi-Jaoudeh2.
Abstract
Radiogenomics involves the integration of mineable data from imaging phenotypes with genomic and clinical data to establish predictive models using machine learning. As a noninvasive surrogate for a tumor's in vivo genetic profile, radiogenomics may potentially provide data for patient treatment stratification. Radiogenomics may also supersede the shortcomings associated with genomic research, such as the limited availability of high-quality tissue and restricted sampling of tumoral subpopulations. Interventional radiologists are well suited to circumvent these obstacles through advancements in image-guided tissue biopsies and intraprocedural imaging. Comprehensive understanding of the radiogenomic process is crucial for interventional radiologists to contribute to this evolving field.Entities:
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Year: 2018 PMID: 29551544 DOI: 10.1016/j.jvir.2017.11.021
Source DB: PubMed Journal: J Vasc Interv Radiol ISSN: 1051-0443 Impact factor: 3.464