Preoperative Prediction of Ancillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence

doi:10.1016/j.acra.2019.11.004

	Preoperative Prediction of Ancillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence
	Tan, Hongna 1,2; Gan, Fuwen 3,4; Wu, Yaping 1,2; Zhou, Jing 1,2; Tian, Jie 5; Lin, Yusong 3,4; Wang, Meiyun 1,2
发表期刊	ACADEMIC RADIOLOGY
ISSN	1076-6332
	2020-09-01
卷号	27 期号:9 页码:1217-1225
通讯作者	Wang, Meiyun(mywang@ha.edu.cn)
摘要	Rationale 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.
关键词	Breast cancer Axillary lymph node Metastasis MRI Radiomics
DOI	10.1016/j.acra.2019.11.004
关键词[WOS]	CANCER STATISTICS ; MRI ; MULTICENTER ; ACCURACY ; NOMOGRAM
收录类别	SCI
语种	英语
资助项目	China 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]
项目资助者	China Postdoctoral Science Foundation ; National Natural Scientific Foundation of China ; Henan Provincial Department of Science and Technology Research Project
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000565915300005
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：41[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/41524
专题	中国科学院分子影像重点实验室
通讯作者	Wang, Meiyun
作者单位	1.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
推荐引用方式 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.