期刊
PHYSICS IN MEDICINE AND BIOLOGY
卷 62, 期 16, 页码 R179-R206出版社
IOP PUBLISHING LTD
DOI: 10.1088/1361-6560/aa7c55
关键词
radiogenomics; outcome modeling; tumor control; normal tissue toxicity; machine learning
资金
- National Institutes of Health [P01 CA059827]
- United States National Institutes of Health [1R01CA134444, HHSN261201500043C]
- American Cancer Society [RSGT-05-200-01-CCE]
- United States Department of Defense [PC074201, PC140371]
- Cancer Research UK
- [K07CA187546]
- Cancer Research UK [18504] Funding Source: researchfish
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据