CASIA OpenIR
Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma
Chen, Jiaming1,2; He, Bingxi3,4,5; Dong, Di3,5; Liu, Ping1,2; Duan, Hui1,2; Li, Weili1,2; Li, Pengfei1,2; Wang, Lu1,2; Fan, Huijian1,2; Wang, Siwen3,5; Zhang, Liwen3,5; Tian, Jie3,5,6; Huang, Zhipei4,5; Chen, Chunlin1,2
Source PublicationBRITISH JOURNAL OF RADIOLOGY
ISSN0007-1285
2020
Volume93Issue:1108Pages:8
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn) ; Huang, Zhipei(zhphuang@ucas.ac.cn) ; Chen, Chunlin(ccl1@smu.edu.cn)
AbstractObjective: To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma. Methods and materials: A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and kappa test were applied to verify the model. Results: Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-Omm-3D_glcm_Idn (p 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 - 0.90) and 0.75 (95% confidence intervall: 0.53 - 0.93) in training and test cohorts, respectively. The kappa coefficient was 0.84, showing excellent consistency. Conclusion: A non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool. Advances in knowledge: A noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.
DOI10.1259/bjr.20190558
WOS KeywordPELVIC LYMPHADENECTOMY ; ENDOMETRIAL CANCER ; COMPLICATIONS ; ADENOCARCINOMA ; ACCURACY ; NOMOGRAM
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC1308701] ; National Key R&D Program of China[2016YFC0103803] ; National Key R&D Program of China[2017YFA0700401] ; National Natural Science Foundation of China[81571422] ; National Natural Science Foundation of China[81370736] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[61622117] ; National Science and Technology Support Program of China[2014BAI05B03] ; National Natural Science Fund of Guangdong[2015A030311024] ; Science and Technology Plan of Guangzhou[158100075] ; Beijing Municipal Science and Technology Commission[Z171100000117023] ; Beijing Municipal Science and Technology Commission[Z161100002616022] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; National Science and Technology Support Program of China ; National Natural Science Fund of Guangdong ; Science and Technology Plan of Guangzhou ; Beijing Municipal Science and Technology Commission ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000521530600007
PublisherBRITISH INST RADIOLOGY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38701
Collection中国科学院自动化研究所
Corresponding AuthorTian, Jie; Huang, Zhipei; Chen, Chunlin
Affiliation1.Southern Med Univ, Nanfang Hosp, Dept Obstet & Gynecol, Guangzhou, Peoples R China
2.Southern Med Univ, Nanfang Hosp, Digital Med Lab, Dept Obstet & Gynecol, Guangzhou, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, Jiaming,He, Bingxi,Dong, Di,et al. Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma[J]. BRITISH JOURNAL OF RADIOLOGY,2020,93(1108):8.
APA Chen, Jiaming.,He, Bingxi.,Dong, Di.,Liu, Ping.,Duan, Hui.,...&Chen, Chunlin.(2020).Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.BRITISH JOURNAL OF RADIOLOGY,93(1108),8.
MLA Chen, Jiaming,et al."Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma".BRITISH JOURNAL OF RADIOLOGY 93.1108(2020):8.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Jiaming]'s Articles
[He, Bingxi]'s Articles
[Dong, Di]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Jiaming]'s Articles
[He, Bingxi]'s Articles
[Dong, Di]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Jiaming]'s Articles
[He, Bingxi]'s Articles
[Dong, Di]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.