CASIA OpenIR  > 中国科学院分子影像重点实验室
Preoperative Prediction of Ancillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence
Tan, Hongna1,2; Gan, Fuwen3,4; Wu, Yaping1,2; Zhou, Jing1,2; Tian, Jie5; Lin, Yusong3,4; Wang, Meiyun1,2
Source PublicationACADEMIC RADIOLOGY
ISSN1076-6332
2020-09-01
Volume27Issue:9Pages:1217-1225
Corresponding AuthorWang, Meiyun(mywang@ha.edu.cn)
AbstractRationale and Objectives: To investigate the value of radiomics method based on the fat-suppressed T2 sequence for preoperative predicting axillary lymph node (ALN) metastasis in breast carcinoma. Materials and Methods: The data of 329 invasive breast cancer patients were divided into the primary cohort (n = 269) and validation cohort (n = 60). Radiomics features were extracted from the fat-suppressed T2-weighted images on breast MRI, and ALN metastasis-related radiomics feature selection was performed using Mann-Whitney U-test and support vector machines with recursive feature elimination; then a radiomics signature was constructed by linear support vector machine. The predictive models were constructed using a linear regression model based on the clinicopathologic factors and radiomics signature, and nomogram was used for a visual prediction of the combined model. The predictive performances are evaluated with the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. Results: A total of 647 radiomics features were extracted from each patient. About 23 ALN metastasis-related radiomics features were selected to construct the radiomics signature, including 17 texture features, 5 first-order statistical features, and one shape feature; patient age, tumor size, HER2 status, and vascular cancer thrombus accompanied or not were selected to construct the cilinicopathologic feature model. The sensitivity, specificity, accuracy, and are under the curve value of radiomics signature, clinicopathologic feature model, and the nomogram were 65.22%, 81.08%, 75.00%, and 0.819 (95% confidence interval [CI]: 0.776-0.861), 30.44%, 81.08%, 61.67%, and 0.605 (95% CI: 0.571-0.624) and 60.87%, 89.19%, 78.33%, and 0.810 (95% CI: 0.761-0.855), respectively. Conclusion: Radiomics methods based on the fat-suppressed T2 sequence and the nomogram are helpful for preoperative accurate predicting ALN metastasis.
KeywordBreast cancer Axillary lymph node Metastasis MRI Radiomics
DOI10.1016/j.acra.2019.11.004
WOS KeywordCANCER STATISTICS ; MRI ; MULTICENTER ; ACCURACY ; NOMOGRAM
Indexed BySCI
Language英语
Funding ProjectChina Postdoctoral Science Foundation[2018M632779] ; National Natural Scientific Foundation of China[81401378] ; National Natural Scientific Foundation of China[81772009] ; Henan Provincial Department of Science and Technology Research Project[201602221] ; Henan Provincial Department of Science and Technology Research Project[182102310162]
Funding OrganizationChina Postdoctoral Science Foundation ; National Natural Scientific Foundation of China ; Henan Provincial Department of Science and Technology Research Project
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000565915300005
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:43[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/41524
Collection中国科学院分子影像重点实验室
Corresponding AuthorWang, Meiyun
Affiliation1.Zhengzhou Univ, Dept Radiol, Henan Prov Peoples Hosp, 7 Rd,Weiwu Rd, Zhengzhou 450003, Henan, Peoples R China
2.Zhengzhou Univ, Imaging Diag Neurol Dis & Res Lab, Henan Prov & Peoples Hosp, 7 Rd,Weiwu Rd, Zhengzhou 450003, Henan, Peoples R China
3.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou 450052, Henan, Peoples R China
4.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450052, Henan, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tan, Hongna,Gan, Fuwen,Wu, Yaping,et al. Preoperative Prediction of Ancillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence[J]. ACADEMIC RADIOLOGY,2020,27(9):1217-1225.
APA Tan, Hongna.,Gan, Fuwen.,Wu, Yaping.,Zhou, Jing.,Tian, Jie.,...&Wang, Meiyun.(2020).Preoperative Prediction of Ancillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence.ACADEMIC RADIOLOGY,27(9),1217-1225.
MLA Tan, Hongna,et al."Preoperative Prediction of Ancillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence".ACADEMIC RADIOLOGY 27.9(2020):1217-1225.
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