CASIA OpenIR
Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer
Xu, Xiaojuan1; Li, Hailin2,3,4; Wang, Siwen2,3; Fang, Mengjie2,3; Zhong, Lianzhen2,3; Fan, Wenwen1; Dong, Di2,3; Tian, Jie2,5; Zhao, Xinming1
Source PublicationFRONTIERS IN ONCOLOGY
ISSN2234-943X
2019-10-09
Volume9Pages:11
Corresponding AuthorDong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhao, Xinming(xinmingzh@sina.com)
AbstractIntroduction: 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.
Keywordendometrial cancer lymph node metastasis magnetic resonance imaging radiomics
DOI10.3389/fonc.2019.01007
WOS KeywordCERVICAL INVASION ; RADIOMIC ANALYSIS ; LYMPHADENECTOMY ; MYOMETRIAL ; CARCINOMA ; NOMOGRAM
Indexed BySCI
Language英语
Funding ProjectBeijing 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] ; 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]
Funding OrganizationBeijing 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
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000497566200001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28827
Collection中国科学院自动化研究所
Corresponding AuthorDong, Di; Tian, Jie; Zhao, Xinming
Affiliation1.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
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Harbin Univ Sci & Technol, Sch Automat, Harbin, Heilongjiang, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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