Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer

doi:10.3389/fonc.2019.01007

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

	Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer
	Xu, Xiaojuan 2; Li, Hailin 1,3,5; Wang, Siwen 1,5; Fang, Mengjie 1,5; Zhong, Lianzhen 1,5; Fan, Wenwen 2; Dong, Di 1,5; Tian, Jie 1,4; Zhao, Xinming 2
刊名	FRONTIERS IN ONCOLOGY
	2019-10-09
卷号	9 页码:11
关键词	endometrial cancer lymph node metastasis magnetic resonance imaging radiomics
ISSN号	2234-943X
DOI	10.3389/fonc.2019.01007
通讯作者	Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhao, Xinming(xinmingzh@sina.com)
英文摘要	Introduction: Assessment of lymph node metastasis (LNM) is crucial for treatment decision and prognosis prediction for endometrial cancer (EC). However, the sensitivity of the routinely used magnetic resonance imaging (MRI) is low in assessing normal-sized LNM (diameter, 0-0.8 cm). We aimed to develop a predictive model based on magnetic resonance (MR) images and clinical parameters to predict LNM in normal-sized lymph nodes (LNs). Materials and Methods: A total of 200 retrospective patients were enrolled and divided into a training cohort (n = 140) and a test cohort (n = 60). All patients underwent preoperative MRI and had pathological result of LNM status. In total, 4,179 radiomic features were extracted. Four models including a clinical model, a radiomic model, and two combined models were built. Area under the receiver operating characteristic (ROC) curves (AUC) and calibration curves were used to assess these models. Subgroup analysis was performed according to LN size. All patients underwent surgical staging and had pathological results. Results: All of the four models showed predictive ability in LNM. One of the combined models, Model(CR1), consisting of radiomic features, LN size, and cancer antigen 125, showed the best discrimination ability on the training cohort [AUC, 0.892; 95% confidence interval [CI], 0.834-0.951] and test cohort (AUC, 0.883; 95% CI, 0.786-0.980). The subgroup analysis showed that this model also indicated good predictive ability in normal-sized LNs (0.3-0.8 cm group, accuracy = 0.846; <0.3 cm group, accuracy = 0.849). Furthermore, compared with the routinely preoperative MR report, the sensitivity and accuracy of this model had a great improvement. Conclusions: A predictive model was proposed based on MR radiomic features and clinical parameters for LNM in EC. The model had a good discrimination ability, especially for normal-sized LNs.
资助项目	Beijing Hope Run Special Fund of Cancer Foundation of China[LC2016B01] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61671449] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1308701] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2016YFC0103803] ; Beijing Municipal Science and Technology Commission[Z171100000117023] ; Beijing Municipal Science and Technology Commission[Z161100002616022] ; Beijing Natural Science Foundation[L182061] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	CERVICAL INVASION ; RADIOMIC ANALYSIS ; LYMPHADENECTOMY ; MYOMETRIAL ; CARCINOMA ; NOMOGRAM
WOS研究方向	Oncology
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000497566200001
资助机构	Beijing Hope Run Special Fund of Cancer Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Municipal Science and Technology Commission ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28826]
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	Dong, Di; Tian, Jie; Zhao, Xinming
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 2.Chinese Acad Med Sci & Peking Union Med Coll, Natl Canc Ctr, Dept Diagnost Imaging, Natl Clin Res Ctr Canc,Canc Hosp, Beijing, Peoples R China 3.Harbin Univ Sci & Technol, Sch Automat, Harbin, Heilongjiang, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xu, Xiaojuan,Li, Hailin,Wang, Siwen,et al. Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer[J]. FRONTIERS IN ONCOLOGY,2019,9:11.
APA	Xu, Xiaojuan.,Li, Hailin.,Wang, Siwen.,Fang, Mengjie.,Zhong, Lianzhen.,...&Zhao, Xinming.(2019).Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer.FRONTIERS IN ONCOLOGY,9,11.
MLA	Xu, Xiaojuan,et al."Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer".FRONTIERS IN ONCOLOGY 9(2019):11.