Literature DB >> 29551544

Radiogenomics and IR.

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.
Copyright © 2017 SIR. Published by Elsevier Inc. All rights reserved.

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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


  2 in total

1.  Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Authors:  Cumali Aktolun
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12       Impact factor: 9.236

2.  Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma.

Authors:  Zhenyin Liu; Jing Zhang
Journal:  BMC Neurol       Date:  2020-06-29       Impact factor: 2.474

  2 in total

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