CASIA OpenIR  > 中国科学院分子影像重点实验室
2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer
Shen, Chen1,2; Liu, Zhenyu2; Guan, Min3,4; Song, Jiangdian2,5; Lian, Yucheng2; Wang, Shuo2; Tang, Zhenchao2,6; Dong, Di6; Kong, Lingfei3,4; Wang, Meiyun3,4; Shi, Dapeng3,4; Tian, Jie1,2,7
Source PublicationTRANSLATIONAL ONCOLOGY
2017-12-01
Volume10Issue:6Pages:886-894
SubtypeArticle
AbstractOBJECTIVE: To compare 2D and 3D radiomics features prognostic performance differences in CT images of nonsmall cell lung cancer (NSCLC). METHOD: We enrolled 588 NSCLC patients from three independent cohorts. Two sets of 463 patients from two different institutes were used as the training cohort. The remaining cohort with 125 patients was set as the validation cohort. A total of 1014 radiomics features (507 2D features and 507 3D features correspondingly) were assessed. Based on the dichotomized survival data, 2D and 3D radiomics indicators were calculated for each patient by trained classifiers. We used the area under the receiver operating characteristic curve (AUC) to assess the prediction performance of trained classifiers (the support vector machine and logistic regression). Kaplan-Meier and Cox hazard survival analyses were also employed. Harrell's concordance index (CIndex) and Akaike's information criteria (AIC) were applied to assess the trained models. RESULTS: Radiomics indicators were built and compared by AUCs. In the training cohort, 2D_AUC = 0.653, 3D_AUC = 0.671. In the validation cohort, 2D_AUC = 0.755, 3D_AUC = 0.663. Both 2D and 3D trained indicators achieved significant results (P < .05) in the Kaplan-Meier analysis and Cox regression. In the validation cohort, 2D Cox model had a CIndex = 0.683 and AIC = 789.047; 3D Cox model obtained a C-Index = 0.632 and AIC = 799.409. CONCLUSION: Both 2D and 3D CT radiomics features have a certain prognostic ability in NSCLC, but 2D features showed better performance in our tests. Considering the cost of the radiomics features calculation, 2D features are more recommended for use in the current study.
KeywordRadiomics
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.tranon.2017.08.007
WOS KeywordPREDICT SURVIVAL ; REPRODUCIBILITY ; PHENOTYPE ; SIGNATURE ; MODELS ; IMAGES
Indexed BySCI
Language英语
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000415323200003
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20347
Collection中国科学院分子影像重点实验室
Affiliation1.Xidian Univ, Sch Life Sci & Technol, Xian 710126, Shaanxi, Peoples R China
2.Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Radiol, Zhengzhou 450003, Henan, Peoples R China
4.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China
5.Northeastern Univ, Sinodutch Biomed & Informat Engn Sch, Shenyang 110819, Liaoning, Peoples R China
6.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Shandong, Peoples R China
7.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Shen, Chen,Liu, Zhenyu,Guan, Min,et al. 2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer[J]. TRANSLATIONAL ONCOLOGY,2017,10(6):886-894.
APA Shen, Chen.,Liu, Zhenyu.,Guan, Min.,Song, Jiangdian.,Lian, Yucheng.,...&Tian, Jie.(2017).2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer.TRANSLATIONAL ONCOLOGY,10(6),886-894.
MLA Shen, Chen,et al."2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer".TRANSLATIONAL ONCOLOGY 10.6(2017):886-894.
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