El Naqa Issam, Kerns Sarah L, Coates James, Luo Yi, Speers Corey, West Catharine M L, Rosenstein Barry S, Ten Haken Randall K
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America.
Phys Med Biol. 2017 Aug 1;62(16):R179-R206. doi: 10.1088/1361-6560/aa7c55.
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.
患者特异性信息和生物技术的进步推动了计算医学的新时代。放射基因组学已成为一个新领域，研究遗传学在放射治疗反应中的作用。放射肿瘤学目前正试图接受这些最新进展，并通过在肿瘤反应建模中保持其作为定量领导者的突出地位，为其丰富的历史增添光彩。在此，我们概述放射基因组学，首先介绍基因分型、数据汇总，以及基于修改传统放射生物学方法或应用先进机器学习技术的不同建模方法的应用。我们强调了这一新领域在重塑放射治疗结果建模格局和推动计算肿瘤学未来进展方面的现状和潜力。
