Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children

doi:10.1186/s12880-019-0355-z

	Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
	Wang, Bei 1; Li, Min 2,3; Ma, He 2; Han, Fangfang 2; Wang, Yan 1; Zhao, Shunying 4; Liu, Zhimin 1; Yu, Tong 1; Tian, Jie 3,5; Dong, Di3,6 ; Peng, Yun 1,3
发表期刊	BMC MEDICAL IMAGING
ISSN	1471-2342
	2019-08-08
卷号	19 期号:1 页码:11
通讯作者	Dong, Di(di.dong@ia.ac.cn) ; Peng, Yun(ppengyun@hotmail.com)
摘要	Background To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. Methods This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912-1) was better than the senior radiologist's clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677-0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889-1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. Conclusions A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.
关键词	Child Tuberculosis Pulmonary Pneumonia Radiomics Nomogram
DOI	10.1186/s12880-019-0355-z
关键词[WOS]	DIAGNOSIS ; CLASSIFICATION ; RADIOMICS
收录类别	SCI
语种	英语
资助项目	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[2016YFA0100900] ; National Key R&D Program of China[2016YFA0100902] ; National Natural Science Foundation of China[81771924] ; 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] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Open Foundation of The State Key Laboratory for Management and Control of Complex Systems[20170110] ; Youth Innovation Promotion Association CAS[2017175] ; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority[XTCX201814] ; 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[2016YFA0100900] ; National Key R&D Program of China[2016YFA0100902] ; National Natural Science Foundation of China[81771924] ; 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] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Open Foundation of The State Key Laboratory for Management and Control of Complex Systems[20170110] ; Youth Innovation Promotion Association CAS[2017175] ; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority[XTCX201814]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Open Foundation of The State Key Laboratory for Management and Control of Complex Systems ; Youth Innovation Promotion Association CAS ; Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000480533000001
出版者	BMC
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：20[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/173211/26348
专题	中国科学院分子影像重点实验室
通讯作者	Dong, Di; Peng, Yun
作者单位	1.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Radiol, 56 Nanlishi Rd, Beijing 100045, Peoples R China 2.Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, 3-11 Wenhua Rd, Shenyang, Liaoning, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 4.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Resp Med, 56 Nanlishi Rd, Beijing 100045, Peoples R China 5.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, 37 Xueyuan Rd, Beijing 100191, Peoples R China 6.Univ Chinese Acad Sci, 19 Yuquan Rd, Beijing, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Bei,Li, Min,Ma, He,et al. Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children[J]. BMC MEDICAL IMAGING,2019,19(1):11.
APA	Wang, Bei.,Li, Min.,Ma, He.,Han, Fangfang.,Wang, Yan.,...&Peng, Yun.(2019).Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.BMC MEDICAL IMAGING,19(1),11.
MLA	Wang, Bei,et al."Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children".BMC MEDICAL IMAGING 19.1(2019):11.