Frontiers in Oncology (Mar 2022)
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
- 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
Affiliations
- Huiting Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Yijie Dong
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Xiaohong Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Jingwen Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Zhiyao Li
- Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China
- Zhirui Chuan
- Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China
- Yanjun Xu
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Institute of Ultrasound in Medicine, Shanghai, China
- Bin Hu
- Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
- Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
- Jinfeng Xu
- Department 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, China
- Fajin Dong
- Department 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, China
- Xiaona Xia
- Department of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Chengrong Wu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Wenjia Hu
- Department of Ultrasound, People’s Hospital of Henan Province, Zhengzhou, China
- Gang Wu
- Department of Ultrasound, People’s Hospital of Henan Province, Zhengzhou, China
- Qiaoying Li
- Department of Ultrasound Diseases, Tangdu Hospital, Four Military Medical University, Xi’an, China
- Qin Chen
- 0Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Wanyue Deng
- 0Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Qiongchao Jiang
- 1Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Yonglin Mou
- 2Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, China
- Huannan Yan
- 2Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, China
- Xiaojing Xu
- 3Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Hongju Yan
- 3Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Ping Zhou
- 4Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China
- Yang Shao
- 4Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China
- Ligang Cui
- 5Department of Ultrasound, Peking University Third Hospital, Beijing, China
- Ping He
- 5Department of Ultrasound, Peking University Third Hospital, Beijing, China
- Linxue Qian
- 6Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Jinping Liu
- 6Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Liying Shi
- 7Department of Ultrasound, Affiliated Hospital of Guizhou Medical University, Guizhou, China
- Yanan Zhao
- 8Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
- Yongyuan Xu
- 8Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
- Yanyan Song
- 9Department of Biostatistics, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- DOI
- https://doi.org/10.3389/fonc.2022.830910
- Journal volume & issue
-
Vol. 12
Abstract
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.
Keywords
