2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer
文献类型：期刊论文
| 作者 | 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
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| 刊名 | TRANSLATIONAL ONCOLOGY
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| 出版日期 | 2017-12-01 |
| 卷号 | 10期号:6页码:886-894 |
| 关键词 | Radiomics |
| DOI | 10.1016/j.tranon.2017.08.007 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | PREDICT SURVIVAL ; REPRODUCIBILITY ; PHENOTYPE ; SIGNATURE ; MODELS ; IMAGES |
| WOS研究方向 | Oncology |
| 语种 | 英语 |
| WOS记录号 | WOS:000415323200003 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20347]  |
| 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.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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