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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
2017-12-01
发表期刊TRANSLATIONAL ONCOLOGY
卷号10期号:6页码:886-894
文章类型Article
摘要OBJECTIVE: 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.
关键词Radiomics
WOS标题词Science & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.tranon.2017.08.007
关键词[WOS]PREDICT SURVIVAL ; REPRODUCIBILITY ; PHENOTYPE ; SIGNATURE ; MODELS ; IMAGES
收录类别SCI
语种英语
WOS研究方向Oncology
WOS类目Oncology
WOS记录号WOS:000415323200003
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20347
专题中国科学院分子影像重点实验室
作者单位1.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
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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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