Radiogenomics and radiotherapy response modeling. (1st August 2017)

• Record Type: Journal Article
• Authors: El Naqa, Issam; Kerns, Sarah L; Coates, James; Luo, Yi; Speers, Corey; West, Catharine M L; Rosenstein, Barry S; Ten Haken, Randall K
• Is Part Of: Physics in medicine & biology. Volume 62:Number 16(2017:Aug.)
• Page Start: R179
• Page End: R206
• Publication Date: 2017-08-01
• Subjects: radiogenomics -- outcome modeling -- tumor control -- normal tissue toxicity -- machine learning; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa7c55
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.