| Zhang Shuming,Li Jiaqi,Wang Hao,et al.Application of machine learningin predicting the outcomes and complications of radiotherapy[J].Chinese Journal of Radiological Medicine and Protection,2018,38(10):792-795 |
| Application of machine learningin predicting the outcomes and complications of radiotherapy |
| Received:February 01, 2018 |
| DOI：10.3760/cma.j.issn.0254-5098.2018.10.015 |
| KeyWords:Machine learning Radiotherapy Prognosis Complication |
| FundProject:国家自然科学基金（81071237，81372420） |
| Author Name | Affiliation | E-mail | | Zhang Shuming | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | | | Li Jiaqi | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | | | Wang Hao | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | | | Jiang Rongtao | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China | | | Sui Jing | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China | | | Shi Chengyu | Memorial Sloan-Kettering Cancer Center, New York NY 10065, United States of America | | | Yang Ruijie | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China | ruijyang@yahoo.com |
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| Abstract:: |
| Machine learning has developed rapidly in recent years. Using machine learning to predict the radiotherapy outcomes and complications can more accurately evaluate the patients' conditions and take appropriate treatment measures as soon as possible. The non-dose and dose related factors generated during radiotherapy are filtered and input into the algorithm model, then corresponding prediction result can be obtained. There are many algorithm models to predict survival rate, tumor control rate and radiotherapy complications, and the predicted result are more accurate now. However, the algorithm model also has various problems, and it needs constant exploration and improvement. |
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