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Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis

文献类型：期刊论文

作者Song, Jiangdian1,2; Liu, Zaiyi3; Zhong, Wenzhao4; Huang, Yanqi3; Ma, Zelan3; Dong, Di2,5; Liang, Changhong3; Tian, Jie2,5
刊名Scientific Reports
出版日期2016-12-06
卷号6期号:38282页码:1-9
关键词Radiomics
DOI10.1038/srep38282
文献子类Article
英文摘要This was a retrospective study to investigate the predictive and prognostic ability of quantitative computed tomography phenotypic features in patients with non-small cell lung cancer (NSCLC). 661 patients with pathological confirmed as NSCLC were enrolled between 2007 and 2014. 592 phenotypic descriptors was automatically extracted on the pre-therapy CT images. Firstly, support vector machine (SVM) was used to evaluate the predictive value of each feature for pathology and TNM clinical stage. Secondly, Cox proportional hazards model was used to evaluate the prognostic value of these imaging signatures selected by SVM which subjected to a primary cohort of 138 patients, and an external independent validation of 61 patients. The results indicated that predictive accuracy for histopathology, N staging, and overall clinical stage was 75.16%, 79.40% and 80.33%, respectively. Besides, Cox models indicated the signatures selected by SVM: "correlation of co-occurrence after wavelet transform" was significantly associated with overall survival in the two datasets (hazard ratio [HR]: 1.65, 95% confidence interval [CI]: 1.41-2.75, p = 0.010; and HR: 2.74, 95% CI: 1.10-6.85, p = 0.027, respectively). Our study indicates that the phenotypic features might provide some insight in metastatic potential or aggressiveness for NSCLC, which potentially offer clinical value in directing personalized therapeutic regimen selection for NSCLC.
WOS关键词TEXTURE ANALYSIS ; TUMOR HETEROGENEITY ; SURVIVAL ; CHEMOTHERAPY ; FEATURES ; PREDICTION ; PARAMETERS ; CARCINOMA ; BIOMARKER ; NODULES
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000389420800001
资助机构National Natural Science Foundation of China(81227901 ; Guangdong Province-Chinese Academy of Science(2012B090400039) ; National Basic Research Program of China(61302025 ; Chinese Academy of Sciences Key deployment program(KGZD-EW-T03) ; Instrument Developing Project of the Chinese Academy of Sciences(YZ201502) ; 81501549 ; 61301002) ; 61231004 ; 81501616 ; 81301346)
源URL[http://ir.ia.ac.cn/handle/173211/13371]  
专题自动化研究所_中国科学院分子影像重点实验室
作者单位1.Northeastern Univ, Sinodutch Biomed & Informat Engn Sch, Shenyang, Liaoning, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Guangdong Gen Hosp, Guangdong Acad Med Sci, Dept Radiol, 106 Zhongshan Er Rd, Guangzhou 510080, Guangdong, Peoples R China
4.Guangdong Acad Med Sci, Guangdong Lung Canc Inst, Guangdong Gen Hosp, 106 Zhongshan Er Lu, Guangzhou 510080, Peoples R China
5.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Song, Jiangdian,Liu, Zaiyi,Zhong, Wenzhao,et al. Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis[J]. Scientific Reports,2016,6(38282):1-9.
APA Song, Jiangdian.,Liu, Zaiyi.,Zhong, Wenzhao.,Huang, Yanqi.,Ma, Zelan.,...&Tian, Jie.(2016).Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis.Scientific Reports,6(38282),1-9.
MLA Song, Jiangdian,et al."Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis".Scientific Reports 6.38282(2016):1-9.

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来源：自动化研究所

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