Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19

doi:10.7150/thno.46428

	Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19
	Wu, Qingxia 1; Wang, Shuo 2; Liang, Li 3; Wu, Qingxia 4,5,6; Qian, Wei 7; Hu, Yahua 8; Li, Li 9; Zhou, Xuezhi 10; Ma, He 1; Li, Hongjun 9; Wang, Meiyun 4,5,6; Qiu, Xiaoming 8; Zha, Yunfei 3; Tian, Jie 1,2,10,11
发表期刊	THERANOSTICS
ISSN	1838-7640
	2020
卷号	10 期号:16 页码:7231-7244
通讯作者	Ma, He(mahe@bmie.neu.edu.cn) ; Li, Hongjun(lihongjun00113@126.com) ; Wang, Meiyun(mywang@ha.edu.cn) ; Qiu, Xiaoming(qxm2020cov@163.com) ; Zha, Yunfei(zhayunfei999@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19. Methods: From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (>= 7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups. Results: In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low- or high-RadScore groups with significantly different survival time in the training and validation cohorts (all P < 0.05). The CrrScore in both groups can also significantly stratify patients with different prognoses regarding different age, type, and comorbidities subgroups in the combined cohorts (all P < 0.05). Conclusions: This research proposed a non-invasive and quantitative prognostic tool for predicting poor outcome in patients with COVID-19 based on CT imaging. Taking the insufficient medical recourse into account, our study might suggest that the chest CT radiomics signature of COVID-19 is more effective and ideal to predict poor outcome in the late-phase COVID-19 patients. For the early-phase patients, integrating radiomics signature with clinical risk factors can achieve a more accurate prediction of individual poor prognostic outcome, which enables appropriate management and surveillance of COVID-19.
关键词	COVID-19 Computed tomography Radiomics Prognosis Poor outcome
DOI	10.7150/thno.46428
关键词[WOS]	CHEST CT ; RISK ; PNEUMONIA ; SURVIVAL ; IMAGES ; COHORT ; GUIDE ; SCANS ; LUNG
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81871332] ; National Natural Science Foundation of China[61936013] ; National Natural Science Foundation of China[81771806] ; National Key R&D Program of China[2017YFA0205200] ; Fundamental Research Funds for the Central Universities[2042020kfxg10] ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province[2020FCA015] ; Hubei Health Committee General Program and Anti-schistosomiasis Fund during 2019-2020[WJ2019M043] ; Beijing Municipal Commission of health[2020-TG-002] ; Youan Medical Development Fund[BJYAYY-2020YC-03]
项目资助者	National Natural Science Foundation of China ; National Key R&D Program of China ; Fundamental Research Funds for the Central Universities ; Novel Coronavirus Pneumonia Emergency Key Project of Science and Technology of Hubei Province ; Hubei Health Committee General Program and Anti-schistosomiasis Fund during 2019-2020 ; Beijing Municipal Commission of health ; Youan Medical Development Fund
WOS研究方向	Research & Experimental Medicine
WOS类目	Medicine, Research & Experimental
WOS记录号	WOS:000545974700013
出版者	IVYSPRING INT PUBL
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：74[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/173211/40060
专题	中国科学院分子影像重点实验室
通讯作者	Ma, He; Li, Hongjun; Wang, Meiyun; Qiu, Xiaoming; Zha, Yunfei; Tian, Jie
作者单位	1.Northeastern Univ, Coll Med & Biomed Informat Engn, Shenyang 110819, Liaoning, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China 3.Wuhan Univ, Dept Radiol, Renmin Hosp, 238 Jiefang Rd, Wuhan 430060, Peoples R China 4.Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou 450003, Henan, Peoples R China 5.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China 6.Henan Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China 7.Univ Texas El Paso, Dept Elect & Comp Engn, 500 West Univ Ave, El Paso, TX 79968 USA 8.Hubei Polytech Univ, Dept Radiol, Huangshi Cent Hosp, Edong Healthcare Grp,Affiliated Hosp, Huangshi 435000, Hubei, Peoples R China 9.Capital Med Univ, Beijing Youan Hosp, Dept Radiol, Beijing 100069, Peoples R China 10.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian 710126, Shaanxi, Peoples R China 11.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, 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	Wu, Qingxia,Wang, Shuo,Liang, Li,et al. Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19[J]. THERANOSTICS,2020,10(16):7231-7244.
APA	Wu, Qingxia.,Wang, Shuo.,Liang, Li.,Wu, Qingxia.,Qian, Wei.,...&Tian, Jie.(2020).Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19.THERANOSTICS,10(16),7231-7244.
MLA	Wu, Qingxia,et al."Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19".THERANOSTICS 10.16(2020):7231-7244.