中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 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

文献类型：期刊论文

作者Xu, Xiaojuan4; Li, Hailin1,2,5; Wang, Siwen1,5; Fang, Mengjie1,5; Zhong, Lianzhen1,5; Fan, Wenwen4; Dong, Di1,5; Tian, Jie1,3; Zhao, Xinming4
刊名FRONTIERS IN ONCOLOGY
出版日期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
DOI10.3389/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.
WOS关键词CERVICAL INVASION ; RADIOMIC ANALYSIS ; LYMPHADENECTOMY ; MYOMETRIAL ; CARCINOMA ; NOMOGRAM
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研究方向Oncology
语种英语
WOS记录号WOS:000497566200001
出版者FRONTIERS MEDIA SA
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
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.Harbin Univ Sci & Technol, Sch Automat, Harbin, Heilongjiang, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
4.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
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。