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Advances of Radiomics and Genomics in Cancer Management

M Giger1*, J Deasy2*, I Tai3*, F Yin4*, (1) University of Chicago, Chicago, IL, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) BCCancer Agency At Vancouver, Vancouver, BC, CA, (4) Duke University Medical Center, Chapel Hill, NC


(Wednesday, 7/15/2020) 1:00 PM - 3:00 PM [Eastern Time (GMT-4)]

Room: Track 2

Genomics and radiomics are two rapidly developing fields and are also being integrated as radiogenomics, which use extracted genomic and radiographic phenotypes/features as biomarkers for disease prediction and treatment assessment in precision medicine. Many promising techniques and models are being developed and encouraging results have been presented for potential cancer prediction, diagnosis, and treatment. However, their clinical efficacy and implementation are yet to be developed mainly due to lack of basic understanding about the technologies and techniques, short of resources and costs of implementation, insufficient clinical outcomes of dedicated clinical trials, and quality assurance methodology. As such, these advanced treatment assessment technologies and techniques have not been widely applied in the era of precision medicine. This session will provide an expert overview of cutting-edge development using genomics and radiomics technologies for cancer screening, diagnosis and treatment assessment using both radiotherapy and chemotherapy. Critical reviews will also involve discussions of strategies for applications and modeling, methodology for data integration, basic and clinical application of using genomics for disease screening and treatment assessment, and challenges and opportunities using radiomics, genomics and deep learning for cancer management.

Learning Objectives:
1. Discuss the recent development in genomics and radiomics, in cancer screening, diagnosis, and treatment assessment
2. Learn strategies and potential QAs of applying radiomics and genomics technologies radiogenomics technologies and techniques in clinical applications.
3. Understand the opportunities and challenges of using radiomics, genomics and deep learning in cancer management

Funding Support, Disclosures, and Conflict of Interest: ML Giger: stockholder:R2 Technology/Hologic and a cofounder and equity holder in Quantitative Insights (now Qlarity Imaging). Receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. Fang-Fang Yin: NIH grants and research grants from Varian Medical Systems E Tai: none



Decision Theory, CAD


TH- Radiobiology(RBio)/Biology(Bio): Rbio - Outcome models combining dose, imaging, radiomics/radiogenomics and clinical factors: machine learning

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