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Radiogenomics and radiotherapy response modeling

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

  1. National Institutes of Health [P01 CA059827]
  2. United States National Institutes of Health [1R01CA134444, HHSN261201500043C]
  3. American Cancer Society [RSGT-05-200-01-CCE]
  4. United States Department of Defense [PC074201, PC140371]
  5. Cancer Research UK
  6. [K07CA187546]
  7. Cancer Research UK [18504] Funding Source: researchfish

向作者/读者索取更多资源

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.

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