title: Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis.
abstract: 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.
authors.affiliation: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Liaoning, Shenyang, 110819, China.|The Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Jiangdian
authors.initials: J
authors.lastname: Song
authors.affiliation: Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
authors.forename: Zaiyi
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Lu, Guangzhou, 510080, China.
authors.forename: Wenzhao
authors.initials: W
authors.lastname: Zhong
authors.affiliation: Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
authors.forename: Yanqi
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
authors.forename: Zelan
authors.initials: Z
authors.lastname: Ma
authors.affiliation: The Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.|Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.
authors.forename: Di
authors.initials: D
authors.lastname: Dong
authors.affiliation: Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
authors.forename: Changhong
authors.initials: C
authors.lastname: Liang
authors.affiliation: The Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.|Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
mesh_terms: D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000401:mortality / Q000473:pathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000401:mortality / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D012189:Retrospective Studies; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed