| Ma Zeliang,Men Kuo,Jiang Haihang,Hui Zhouguang.Clinical application of machine learning in radiation oncology[J].Chinese Journal of Radiological Medicine and Protection,2021,41(2):155-159 |
| Clinical application of machine learning in radiation oncology |
| Received:April 19, 2020 |
| DOI：10.3760/cma.j.issn.0254-5098.2021.02.014 |
| KeyWords:Machine learning Artificial intelligence Radiation oncology |
| FundProject:国家重点研发项目（2017YFC1311000，2017YFC1311002，2018YFC0116800） |
| Author Name | Affiliation | E-mail | | Ma Zeliang | Department of Radiation Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China | | | Men Kuo | Department of Radiation Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China | | | Jiang Haihang | Suzhou Xunzheng Medical Technology Co., Ltd. Department of Research and Development, Suzhou 215000, China | | | Hui Zhouguang | Department of VIP Medical Services, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China | drhuizg@163.com |
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| Abstract:: |
| Radiation therapy is one of the main treatment methods for cancer. Machine learning can be used in all aspects of clinical practice in radiation therapy, including clinical decision support, automatic segmentation of target volumes, prediction of treatment efficacy and side effects. Despite the challenges of lacking structured data and poor interpretability of models, the application of machine learning in radiotherapy will become increasingly profound and extensive. This review contains three aspects: introduction of machine learning, the clinical application of machine learning in radiotherapy, challenges and solutions. |
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