CASIA OpenIR  > 中国科学院分子影像重点实验室
Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma
Zhang, Bin1,2; He, Xin3; Ouyang, Fusheng4; Gu, Dongsheng5; Dong, Yuhao6,7; Zhang, Lu6; Mo, Xiaokai6,7; Huang, Wenhui6,8; Tian, Jie5; Zhang, Shuixing1,2
2017-09-10
发表期刊CANCER LETTERS
卷号403页码:21-27
文章类型Article
摘要We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10 fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 +/- 0.0069; test error, 03135 +/- 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 +/- 0.0095; test error, 0.3384 +/- 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 +/- 0.0096; test error, 0.3985 +/- 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice. (C) 2017 Elsevier B.V. All rights reserved.
关键词Radiomics Imaging Nasopharyngeal Carcinoma Machine-learning
WOS标题词Science & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.canlet.2017.06.004
关键词[WOS]TUMOR PHENOTYPE ; LUNG-CANCER ; FEATURES ; HETEROGENEITY ; IMAGES ; MRI ; RECONSTRUCTION ; CHALLENGES ; PET
收录类别SCI
语种英语
项目资助者National Scientific Foundation of China(81571664) ; Science and Technology Planning Project of Guangdong Province(2014A020212244 ; Commission on Innovation and Technology of Guangdong Province(201605110912158) ; Clinical Research Foundation of Guangdong General Hospital(2015zh04) ; 2016A020216020)
WOS研究方向Oncology
WOS类目Oncology
WOS记录号WOS:000407662300003
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20725
专题中国科学院分子影像重点实验室
作者单位1.Jinan Univ, Affiliated Hosp 1, Med Imaging Ctr, Guangzhou, Guangdong, Peoples R China
2.Jinan Univ, Inst Mol & Funct Imaging, Guangzhou, Guangdong, Peoples R China
3.City Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
4.First Peoples Hosp Shunde, Dept Radiol, Foshan, Guangdong, Peoples R China
5.Chinese Acad Sci, Key Lab Mol Imaging, Beijing, Peoples R China
6.Guangdong Acad Med Sci, Guangdong Gen Hosp, Dept Radiol, Guangzhou, Guangdong, Peoples R China
7.Shantou Univ, Med Coll, Shantou, Guangdong, Peoples R China
8.South China Univ Technol, Sch Med, Guangzhou, Guangdong, Peoples R China
推荐引用方式
GB/T 7714
Zhang, Bin,He, Xin,Ouyang, Fusheng,et al. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma[J]. CANCER LETTERS,2017,403:21-27.
APA Zhang, Bin.,He, Xin.,Ouyang, Fusheng.,Gu, Dongsheng.,Dong, Yuhao.,...&Zhang, Shuixing.(2017).Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.CANCER LETTERS,403,21-27.
MLA Zhang, Bin,et al."Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma".CANCER LETTERS 403(2017):21-27.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhang, Bin]的文章
[He, Xin]的文章
[Ouyang, Fusheng]的文章
百度学术
百度学术中相似的文章
[Zhang, Bin]的文章
[He, Xin]的文章
[Ouyang, Fusheng]的文章
必应学术
必应学术中相似的文章
[Zhang, Bin]的文章
[He, Xin]的文章
[Ouyang, Fusheng]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。