CASIA OpenIR
Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer
Kan, Yangyang1,2,3; Dong, Di4,5; Zhang, Yuchen6; Jiang, Wenyan2,3; Zhao, Nannan2,3; Han, Lu2,3; Fang, Mengjie4,5; Zang, Yali4,5; Hu, Chaoen4,5; Tian, Jie4,5; Li, Chunming6; Luo, Yahong1,2,3
Source PublicationJOURNAL OF MAGNETIC RESONANCE IMAGING
ISSN1053-1807
2019
Volume49Issue:1Pages:304-310
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn) ; Li, Chunming(li_chunming@hotmail.com) ; Luo, Yahong(Luoyahong8888@hotmail.com)
AbstractBackground Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. Purpose To evaluate a radiomic signature of LN involvement based on sagittal T-1 contrast-enhanced (CE) and T-2 MRI sequences. Study Type Retrospective. Population In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. Field Strength/Sequence T-1 CE and T-2 MRI sequences at 3T. Assessment The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. Statistical Tests A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. Results The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. Data Conclusions A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.
DOI10.1002/jmri.26209
WOS KeywordSURVIVAL ; ENDOMETRIAL ; CARCINOMA ; NEOPLASMS ; CT
Indexed BySCI
Language英语
Funding ProjectNational 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[81601492] ; 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] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Beijing Municipal Science and Technology Commission[Z161100002616022] ; Youth Innovation Promotion Association CAS ; Special Fund for Research in the Public Interest of China[201402020]
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Beijing Municipal Science and Technology Commission ; Youth Innovation Promotion Association CAS ; Special Fund for Research in the Public Interest of China
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000453908200027
PublisherWILEY
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25658
Collection中国科学院自动化研究所
Corresponding AuthorTian, Jie; Li, Chunming; Luo, Yahong
Affiliation1.Dalian Med Univ, Dalian, Peoples R China
2.China Med Univ, Canc Hosp, Shenyang, Liaoning, Peoples R China
3.Liaoning Canc Hosp & Inst, Shenyang, Liaoning, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kan, Yangyang,Dong, Di,Zhang, Yuchen,et al. Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2019,49(1):304-310.
APA Kan, Yangyang.,Dong, Di.,Zhang, Yuchen.,Jiang, Wenyan.,Zhao, Nannan.,...&Luo, Yahong.(2019).Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer.JOURNAL OF MAGNETIC RESONANCE IMAGING,49(1),304-310.
MLA Kan, Yangyang,et al."Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer".JOURNAL OF MAGNETIC RESONANCE IMAGING 49.1(2019):304-310.
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
[Kan, Yangyang]'s Articles
[Dong, Di]'s Articles
[Zhang, Yuchen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Kan, Yangyang]'s Articles
[Dong, Di]'s Articles
[Zhang, Yuchen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Kan, Yangyang]'s Articles
[Dong, Di]'s Articles
[Zhang, Yuchen]'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.