Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma

doi:10.1016/j.canlet.2017.06.004

	Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma
	Zhang, Bin1,2 ; He, Xin 3; Ouyang, Fusheng 4; Gu, Dongsheng 5; Dong, Yuhao 6,7; Zhang, Lu 6; Mo, Xiaokai 6,7; Huang, Wenhui 6,8; Tian, Jie 5; Zhang, Shuixing 1,2
发表期刊	CANCER LETTERS
	2017-09-10
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
DOI	10.1016/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	被引频次：178[WOS] [WOS记录] [WOS相关记录]
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.