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
Source PublicationCANCER LETTERS
2017-09-10
Volume403Pages:21-27
SubtypeArticle
AbstractWe 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.
KeywordRadiomics Imaging Nasopharyngeal Carcinoma Machine-learning
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.canlet.2017.06.004
WOS KeywordTUMOR PHENOTYPE ; LUNG-CANCER ; FEATURES ; HETEROGENEITY ; IMAGES ; MRI ; RECONSTRUCTION ; CHALLENGES ; PET
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000407662300003
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20725
Collection中国科学院分子影像重点实验室
Affiliation1.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
Recommended Citation
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.
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