Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study | |
Meng, Lingwei1,2; Dong, Di1,2; Chen, Xin3; Fang, Mengjie1,2; Wang, Rongpin4; Li, Jing5; Liu, Zaiyi6; Tian, Jie1,7 | |
发表期刊 | IEEE Journal of Biomedical and Health Informatics |
ISSN | 2168-2194 |
2020 | |
卷号 | 25期号:3页码:755-762 |
文章类型 | article |
摘要 | Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks (T LNM , lymph node metastasis' prediction; T LVI , lymphovascular invasion's prediction; T pT , pT4 or other pT stages' classification). Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model LNM 2D , Model LNM 3D ; Model LVI 2D , Model LVI 3D s Model pT 2D ,s Model pT 3D ) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing different. Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: Model LNM2D 's 0.712 (95% confidence interval, 0.613-0.811), Model LNM 3D 's 0.680 (0.584-0.775); Model LVI 2D 's 0.677 (0.595-0.761), Model LVI 3D 's 0.615 (0.528-0.703); Model pT 2D 's 0.840 (0.779-0.901), Model pT 3D 's 0.813 (0.747-0.879). Moreover, the auxiliary experiment indicated that Models 2D are statistically advantageous than Models 3D with different resampling spacings. Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches. |
关键词 | Computed tomography (CT) |
DOI | 10.1109/JBHI.2020.3002805 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81601469] ; National Natural Science Foundation of China[81771912] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; National Science Fund for Distinguished Young Scholars[81925023] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0700401] ; Science and Technology Planning Project of Guangzhou[201804010032] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Science Fund for Distinguished Young Scholars ; National Key R&D Program of China ; Science and Technology Planning Project of Guangzhou ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS |
WOS研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
WOS类目 | Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics |
WOS记录号 | WOS:000626521100015 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40686 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Liu, Zaiyi; Tian, Jie |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 2.Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China 3.Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China 4.Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China 5.Department of Radiology, Henan Cancer Hospital, Zhengzhou, China 6.Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China 7.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Meng, Lingwei,Dong, Di,Chen, Xin,et al. 2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study[J]. IEEE Journal of Biomedical and Health Informatics,2020,25(3):755-762. |
APA | Meng, Lingwei.,Dong, Di.,Chen, Xin.,Fang, Mengjie.,Wang, Rongpin.,...&Tian, Jie.(2020).2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study.IEEE Journal of Biomedical and Health Informatics,25(3),755-762. |
MLA | Meng, Lingwei,et al."2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study".IEEE Journal of Biomedical and Health Informatics 25.3(2020):755-762. |
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