Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

doi:10.1016/j.tranon.2021.101113

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer
	Liu, Yujia 1,2; Fan, Huijian 3; Dong, Di 1,2,4; Liu, Ping 3; He, Bingxi 2,5; Meng, Lingwei 1,2; Chen, Jiaming 3; Chen, Chunlin 3; Lang, Jinghe 3,6; Tian, Jie 2,4,7
刊名	TRANSLATIONAL ONCOLOGY
	2021-08-01
卷号	14 期号:8 页码:7
关键词	Cervical cancer Lymph node metastasis Radiomics Preoperative prediction Classifiers
ISSN号	1936-5233
DOI	10.1016/j.tranon.2021.101113
通讯作者	Lang, Jinghe(langjh@hotmail.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Purpose: Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography-based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. Methods: A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). Results: Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. Conclusion: We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.
资助项目	National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[82022036] ; 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[6202790004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81272585] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[2017175] ; Strategic Priority CAS Project[XDB38040200] ; National Natural Science Foundation of Guangdong[2015A030311024] ; Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology[201508020264] ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; National Key Technology Program of the Ministry of Science and Technology (863 program)[2014BAI05B03] ; Guangzhou Science and Technology Program[158100075] ; Funding for Highlevel University Construction of the Department of Education of Guangdong Province Clinical Research Initiation Project of Southern Medical University[LC2016ZD019]
WOS关键词	DIAGNOSTIC PERFORMANCE ; NOMOGRAM ; IMAGES
WOS研究方向	Oncology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000687271300001
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; Strategic Priority CAS Project ; National Natural Science Foundation of Guangdong ; Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; National Key Technology Program of the Ministry of Science and Technology (863 program) ; Guangzhou Science and Technology Program ; Funding for Highlevel University Construction of the Department of Education of Guangdong Province Clinical Research Initiation Project of Southern Medical University
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45923]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Lang, Jinghe; Tian, Jie
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Southern Med Univ, Nanfang Hosp, Dept Obstet & Gynecol, Guangzhou 510515, Peoples R China 4.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai 519000, Peoples R China 5.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China 6.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Obstet & Gynecol, 1 Shuaifuyuan Wangfujing Dongcheng Dist, Beijing 100730, Peoples R China 7.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yujia,Fan, Huijian,Dong, Di,et al. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer[J]. TRANSLATIONAL ONCOLOGY,2021,14(8):7.
APA	Liu, Yujia.,Fan, Huijian.,Dong, Di.,Liu, Ping.,He, Bingxi.,...&Tian, Jie.(2021).Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer.TRANSLATIONAL ONCOLOGY,14(8),7.
MLA	Liu, Yujia,et al."Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer".TRANSLATIONAL ONCOLOGY 14.8(2021):7.