| 机器学习在预测放疗疗效及并发症中的应用 |
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| 引用本文： | 张书铭,李佳奇,王皓,姜荣涛,隋婧,石成玉,杨瑞杰. 机器学习在预测放疗疗效及并发症中的应用[J]. 中华放射医学与防护杂志, 2018, 38(10): 792-795 |
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| 作者姓名： | 张书铭 李佳奇 王皓 姜荣涛 隋婧 石成玉 杨瑞杰 |
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| 作者单位： | 100191 北京大学第三医院肿瘤放疗科,100191 北京大学第三医院肿瘤放疗科,100191 北京大学第三医院肿瘤放疗科,100190 北京, 中国科学院自动化研究所 模式识别国家重点实验室,100190 北京, 中国科学院自动化研究所 模式识别国家重点实验室,NY10065 纽约, 美国纽约市纪念斯隆-凯特琳癌症中心,100191 北京大学第三医院肿瘤放疗科 |
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| 基金项目： | 国家自然科学基金（81071237，81372420） |
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| 摘 要： | 近年来，机器学习发展迅速，利用机器学习对放疗后疗效及并发症进行预测，可以更加准确地评估患者病情，及早采取相应治疗措施。将放疗过程中产生的非剂量相关和剂量相关特征值经筛选后输入算法模型，可以得到相应的预测结果。目前，已有多种算法模型可以对放疗后患者生存率、肿瘤控制率及各种放疗后并发症进行预测，预测结果较为准确。但算法模型也存在各种问题，需要不断探索改进。
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| 关 键 词： | 机器学习 放射疗法 预后 并发症 |
| 收稿时间： | 2018-02-01 |
Application of machine learningin predicting the outcomes and complications of radiotherapy |
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| Zhang Shuming,Li Jiaqi,Wang Hao,Jiang Rongtao,Sui Jing,Shi Chengyu and Yang Ruijie. Application of machine learningin predicting the outcomes and complications of radiotherapy[J]. Chinese Journal of Radiological Medicine and Protection, 2018, 38(10): 792-795 |
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| Authors: | Zhang Shuming Li Jiaqi Wang Hao Jiang Rongtao Sui Jing Shi Chengyu Yang Ruijie |
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| Affiliation: | Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China,Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China,Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China,Memorial Sloan-Kettering Cancer Center, New York NY 10065, United States of America and Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China |
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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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| Keywords: | Machine learning Radiotherapy Prognosis Complication |
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