Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8823926 | Journal of Vascular and Interventional Radiology | 2018 | 8 Pages |
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.
Keywords
EGFRBRCA1-associated protein 1BAP1RCCKRASRFSKDM5CMVIRRSHCCHIF1aPFsALKprogression-free survivalRecurrence-free survivaloverall survivalMicrovascular invasionKirsten rat sarcoma viral oncogene homologNSCLCNon-small cell lung cancerVascular endothelial growth factorVascular Endothelial Growth Factor (VEGF)Anaplastic lymphoma kinaseRenal cell carcinomaHepatocellular carcinomaEpidermal growth factor receptor
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Authors
Alexander MD, Kevin MD, Eduardo MD, Michael MD, Dayantha MD, Kari MD, Nadine MD,