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Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer
Tan, Hongna1,2,3; Wu, Yaping1,2,3; Bao, Fengchang3,4; Zhou, Jing1,2,3; Wan, Jianzhong5,6; Tian, Jie7; Lin, Yusong5,6; Wang, Meiyun1,2,3
Source PublicationBRITISH JOURNAL OF RADIOLOGY
ISSN0007-1285
2020
Volume93Issue:1111Pages:11
Corresponding AuthorWang, Meiyun(mywang@ha.edu.cn)
AbstractObjective: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. Methods: 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. Results: 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591-0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). Conclusion: The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. Advances in knowledge: ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.
DOI10.1259/bjr.20191019
WOS KeywordQUALITY-OF-LIFE ; PREOPERATIVE PREDICTION ; MRI ; ULTRASONOGRAPHY ; DISSECTION ; BIOPSY
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:000542990000012
PublisherBRITISH INST RADIOLOGY
Citation statistics
Cited Times:22[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39940
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, Zhengzhou 450003, Henan, Peoples R China
2.Zhengzhou Univ, Imaging Diag Neurol Dis & Res Lab Henan Prov, Zhengzhou 450003, Henan, Peoples R China
3.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China
4.Zhengzhou Univ, Dept Hematol, Henan Prov Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China
5.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou 450052, Henan, Peoples R China
6.Zhengzhou Univ, Sch Software, Zhengzhou 450052, Henan, Peoples R China
7.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tan, Hongna,Wu, Yaping,Bao, Fengchang,et al. Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer[J]. BRITISH JOURNAL OF RADIOLOGY,2020,93(1111):11.
APA Tan, Hongna.,Wu, Yaping.,Bao, Fengchang.,Zhou, Jing.,Wan, Jianzhong.,...&Wang, Meiyun.(2020).Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer.BRITISH JOURNAL OF RADIOLOGY,93(1111),11.
MLA Tan, Hongna,et al."Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer".BRITISH JOURNAL OF RADIOLOGY 93.1111(2020):11.
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