Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study
PurposeTo develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.Materials and MethodsA total of 619 participants pathologically diagnosed with invasive...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.830910/full |
| _version_ | 1819104540127920128 |
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| author | Huiting Zhang Yijie Dong Xiaohong Jia Jingwen Zhang Zhiyao Li Zhirui Chuan Yanjun Xu Bin Hu Yunxia Huang Cai Chang Jinfeng Xu Fajin Dong Xiaona Xia Chengrong Wu Wenjia Hu Gang Wu Qiaoying Li Qin Chen Wanyue Deng Qiongchao Jiang Yonglin Mou Huannan Yan Xiaojing Xu Hongju Yan Ping Zhou Yang Shao Ligang Cui Ping He Linxue Qian Jinping Liu Liying Shi Yanan Zhao Yongyuan Xu Yanyan Song Weiwei Zhan Jianqiao Zhou |
| author_facet | Huiting Zhang Yijie Dong Xiaohong Jia Jingwen Zhang Zhiyao Li Zhirui Chuan Yanjun Xu Bin Hu Yunxia Huang Cai Chang Jinfeng Xu Fajin Dong Xiaona Xia Chengrong Wu Wenjia Hu Gang Wu Qiaoying Li Qin Chen Wanyue Deng Qiongchao Jiang Yonglin Mou Huannan Yan Xiaojing Xu Hongju Yan Ping Zhou Yang Shao Ligang Cui Ping He Linxue Qian Jinping Liu Liying Shi Yanan Zhao Yongyuan Xu Yanyan Song Weiwei Zhan Jianqiao Zhou |
| author_sort | Huiting Zhang |
| collection | DOAJ |
| description | PurposeTo develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.Materials and MethodsA total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.ResultsA significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulusof the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis.ConclusionA 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer. |
| first_indexed | 2024-12-22T02:07:58Z |
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| id | doaj.art-929a39dbeb6c4310aee9167ab6f0826c |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-22T02:07:58Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-929a39dbeb6c4310aee9167ab6f0826c2022-12-21T18:42:29ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-03-011210.3389/fonc.2022.830910830910Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter StudyHuiting Zhang0Yijie Dong1Xiaohong Jia2Jingwen Zhang3Zhiyao Li4Zhirui Chuan5Yanjun Xu6Bin Hu7Yunxia Huang8Cai Chang9Jinfeng Xu10Fajin Dong11Xiaona Xia12Chengrong Wu13Wenjia Hu14Gang Wu15Qiaoying Li16Qin Chen17Wanyue Deng18Qiongchao Jiang19Yonglin Mou20Huannan Yan21Xiaojing Xu22Hongju Yan23Ping Zhou24Yang Shao25Ligang Cui26Ping He27Linxue Qian28Jinping Liu29Liying Shi30Yanan Zhao31Yongyuan Xu32Yanyan Song33Weiwei Zhan34Jianqiao Zhou35Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, ChinaDepartment of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, ChinaDepartment of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Institute of Ultrasound in Medicine, Shanghai, ChinaDepartment of Ultrasound, Minhang Hospital, Fudan University, Shanghai, ChinaDepartment of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Ultrasound, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, and The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Ultrasound, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, and The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, ChinaDepartment of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, ChinaDepartment of Ultrasound, People’s Hospital of Henan Province, Zhengzhou, ChinaDepartment of Ultrasound, People’s Hospital of Henan Province, Zhengzhou, ChinaDepartment of Ultrasound Diseases, Tangdu Hospital, Four Military Medical University, Xi’an, China0Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China0Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China1Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China2Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, China2Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, China3Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China3Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China4Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China4Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China5Department of Ultrasound, Peking University Third Hospital, Beijing, China5Department of Ultrasound, Peking University Third Hospital, Beijing, China6Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China6Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China7Department of Ultrasound, Affiliated Hospital of Guizhou Medical University, Guizhou, China8Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China8Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China9Department of Biostatistics, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaPurposeTo develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.Materials and MethodsA total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.ResultsA significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulusof the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis.ConclusionA 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.https://www.frontiersin.org/articles/10.3389/fonc.2022.830910/fullbreast neoplasmslymphatic metastasisultrasonographyelasticity imaging techniquesrisk assessment |
| spellingShingle | Huiting Zhang Yijie Dong Xiaohong Jia Jingwen Zhang Zhiyao Li Zhirui Chuan Yanjun Xu Bin Hu Yunxia Huang Cai Chang Jinfeng Xu Fajin Dong Xiaona Xia Chengrong Wu Wenjia Hu Gang Wu Qiaoying Li Qin Chen Wanyue Deng Qiongchao Jiang Yonglin Mou Huannan Yan Xiaojing Xu Hongju Yan Ping Zhou Yang Shao Ligang Cui Ping He Linxue Qian Jinping Liu Liying Shi Yanan Zhao Yongyuan Xu Yanyan Song Weiwei Zhan Jianqiao Zhou Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study Frontiers in Oncology breast neoplasms lymphatic metastasis ultrasonography elasticity imaging techniques risk assessment |
| title | Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study |
| title_full | Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study |
| title_fullStr | Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study |
| title_full_unstemmed | Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study |
| title_short | Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study |
| title_sort | comprehensive risk system based on shear wave elastography and bi rads categories in assessing axillary lymph node metastasis of invasive breast cancer a multicenter study |
| topic | breast neoplasms lymphatic metastasis ultrasonography elasticity imaging techniques risk assessment |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.830910/full |
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