CASIA OpenIR
Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer
Wu, Qingxia1,2,3; Wang, Shuo4,5,6; Chen, Xi5,7; Wang, Yan1,2,3; Dong, Li2,3,8; Liu, Zhenyu5,6; Tian, Jie4,5,6,9; Wang, Meiyun1,2,3
Source PublicationRADIOTHERAPY AND ONCOLOGY
ISSN0167-8140
2019-09-01
Volume138Pages:141-148
Corresponding AuthorLiu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Meiyun(mywang@ha.edu.cn)
AbstractBackground and purpose: Robust parameters are needed to predict lymph node metastasis (LNM) in locally advanced cervical cancer patients in order to select optimal treatment regimen. The aim of this study is to utilize radiomics analysis of magnetic resonance imaging (MRI) to improve diagnostic performance of LNM in cervical cancer patients. Materials and methods: A total of 189 cervical cancer patients were divided into a training cohort (n = 126) and a validation cohort (n = 63). For each patient, we extracted radiomic features from intratumoral and peritumoral tissues on sagittal T2WI and axial apparent diffusion coefficient (ADC) maps. Afterward, the radiomic features associated with LNM status were selected by univariate ROC testing and logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, a support vector machine (SVM) model was established to predict LNM status. To further improve the diagnostic performance, a decision tree which combines the radiomics model with clinical factors was built. Results: Radiomics model of the intratumoral and peritumoral tissues on T2WI (T2(tumor+peri)) showed best sensitivity and clinical LN (c-LN) status showed best specificity to predict LNM. The decision tree that combines radiomics model of T2(tumor+peri) and c-LN status achieved best diagnostic performance, with AUC and sensitivity of 0.895 and 94.3%, 0.847 and 100% in the training and validation cohort respectively. Conclusions: The decision tree, which incorporates radiomics model of T2(tumor+peri) and c-LN status can be potentially applied in the preoperative prediction of LNM in locally advanced cervical cancer patients. (C) 2019 Elsevier B.V. All rights reserved.
KeywordRadiomics Cervical cancer Lymph nodes Magnetic resonance imaging
DOI10.1016/j.radonc.2019.04.035
WOS KeywordPREOPERATIVE PREDICTION ; CARCINOMA ; RADIOTHERAPY ; ACCURACY ; NOMOGRAM ; INSIGHTS ; ABSENCE
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFE0103600] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81720108021] ; Beijing Natural Science Foundation[7182109] ; Key Project of Henan Province Medical Science and Technology Project[2018020422] ; Youth Innovation Promotion Association CAS[2019136]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Key Project of Henan Province Medical Science and Technology Project ; Youth Innovation Promotion Association CAS
WOS Research AreaOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000482210600021
PublisherELSEVIER IRELAND LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27592
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Zhenyu; Tian, Jie; Wang, Meiyun
Affiliation1.Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou, Henan, Peoples R China
2.Zhengzhou Univ, Peoples Hosp, Zhengzhou, Henan, Peoples R China
3.Henan Univ, Peoples Hosp, Zhengzhou, Henan, Peoples R China
4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Beijing, Peoples R China
7.Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China
8.Henan Prov Peoples Hosp, Dept Gynaecol, Zhengzhou, Henan, Peoples R China
9.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Shanxi, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Qingxia,Wang, Shuo,Chen, Xi,et al. Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer[J]. RADIOTHERAPY AND ONCOLOGY,2019,138:141-148.
APA Wu, Qingxia.,Wang, Shuo.,Chen, Xi.,Wang, Yan.,Dong, Li.,...&Wang, Meiyun.(2019).Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer.RADIOTHERAPY AND ONCOLOGY,138,141-148.
MLA Wu, Qingxia,et al."Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer".RADIOTHERAPY AND ONCOLOGY 138(2019):141-148.
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