CASIA OpenIR  > 中国科学院分子影像重点实验室
Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis
Song, Jiangdian1,2; Liu, Zaiyi3; Zhong, Wenzhao4; Huang, Yanqi3; Ma, Zelan3; Dong, Di2,5; Liang, Changhong3; Tian, Jie2,5
Source PublicationScientific Reports
2016-12-06
Volume6Issue:38282Pages:1-9
SubtypeArticle
AbstractThis 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.
KeywordRadiomics
WOS HeadingsScience & Technology
DOI10.1038/srep38282
WOS KeywordTEXTURE ANALYSIS ; TUMOR HETEROGENEITY ; SURVIVAL ; CHEMOTHERAPY ; FEATURES ; PREDICTION ; PARAMETERS ; CARCINOMA ; BIOMARKER ; NODULES
Indexed BySCI
Language英语
Funding OrganizationNational 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)
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000389420800001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13371
Collection中国科学院分子影像重点实验室
Affiliation1.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
Recommended Citation
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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