| 应用放射组学和剂量学特征预测食管癌放射治疗后的两年生存情况 |
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| 引用本文： | 杨成文,冯远明,李铭,郭露,王伟. 应用放射组学和剂量学特征预测食管癌放射治疗后的两年生存情况[J]. 中国肿瘤临床, 2020, 47(7): 334-337. DOI: 10.3969/j.issn.1000-8179.2020.07.312 |
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| 作者姓名： | 杨成文 冯远明 李铭 郭露 王伟 |
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| 作者单位： | ①.天津医科大学肿瘤医院放射肿瘤科, 国家肿瘤临床医学研究中心, 天津市肿瘤防治重点实验室, 天津市恶心肿瘤临床医学研究中心(天津市 300060) |
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| 基金项目： | 国家重点研发计划项目2017YFC0113100 |
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| 摘 要： | 目的:使用放射组学与剂量学特征参数,建立机器学习预测模型,预测食管癌患者放射治疗后两年的生存情况。方法:回顾性分析2013年1月至2017年12月在天津医科大学肿瘤医院接受放射治疗的食管癌患者共579例。从食管癌患者的放射治疗计划中提取GTV的放射组学和剂量学的特征,使用最大相关最小冗余与人工方法对特征参数进行筛选,分别选取14项放射组学和14项剂量学特征,并将特征变量进行标准化归一至[0,1]范围。建立支持向量机、逻辑回归和随机森林等机器学习模型,先使用14项放射组学特征,再使用28项放射组学和剂量学混合特征参数进行训练和测试,来预测食管癌放射治疗患者的两年生存情况。结果:仅使用放射组学特征预测放射治疗后两年生存情况时,支持向量机、逻辑回归和随机森林模型的准确率分别为84.98%、85.92%和84.51%。使用放射组学和剂量学的混合特征参数进行预测时,支持向量机、逻辑回归和随机森林模型的准确率分别为86.32%、83.02%和90.01%。在放射组学特征参数基础上,增加剂量学特征,支持向量机和随机森林模型的预测准确性得到有效提高。结论:针对支持向量机和随机森林模型,使用放射组学和放...
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| 关 键 词： | 放射组学 剂量学特征 多参数预测模型 食管癌 放射治疗 两年生存情况 |
| 收稿时间： | 2020-03-31 |
Applying radiomics and dosimetry features to predict 2-year survival of esophageal cancer patients treated with radiotherapy |
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| Chengwen Yang,Yuanming Feng,Ming Li,Lu Guo,Wei Wang. Applying radiomics and dosimetry features to predict 2-year survival of esophageal cancer patients treated with radiotherapy[J]. Chinese Journal of Clinical Oncology, 2020, 47(7): 334-337. DOI: 10.3969/j.issn.1000-8179.2020.07.312 |
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| Authors: | Chengwen Yang Yuanming Feng Ming Li Lu Guo Wei Wang |
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| Affiliation: | ①.Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China②.Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China |
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| Abstract: | Objective Applying radiomics and dosimetry features to establish machine learning models, which is used to predict the 2-year survival of esophageal patients with radiotherapy. Methods Retrospective analysis of 579 esophageal cancer patients who underwent radiotherapy from January 2013 to December 2017 in Tianjin Medical University Cancer Institute and Hospital. Radiomics and dosimetry features were extracted from the GTV of the radiotherapy plan for patients with esophageal cancer. The maximum correlation and minimum redundancy and manual methods were used to reduce the feature vector. A total of 14 radiomics and 14 dosimetry features were selected, then normalized to the range[0, 1]. The machine learning models such as support vector machines (SVM), Logistic regression (LR), and random forest (RF) were used to train and test the radiomics and dosimetry features, respectively, then to predict the 2-year survival of esophageal cancer patients treated with radiotherapy. Results When only the radiomics features were used to predict the 2-year survival after radiotherapy, the accuracy of SVM, LR and RF models were 84.98%, 85.92% and 84.51%, respectively. Furthermore, when the combined features of radiomics and dosimetry were used for prediction, the accuracy of the SVM, LR and RF models were 86.32%, 83.02% and 90.01%, respectively. Using the radiomics and dosimetry features, the predictive accuracy of SVM and RF models are effectively improved. Conclusion For the SVM and RF models, the radiomics and dosimetry features can effectively improve the accuracy of predicting 2-year survival for esophageal cancer patients after radiation therapy. |
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| Keywords: | radiomics dosimetry characteristics multi-parameter predictive model esophageal cancer radiotherapy 2-year survival |
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