1. Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study
- Author
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Yang Gu, Wen Xu, Bin Lin, Xing An, Jiawei Tian, Haitao Ran, Weidong Ren, Cai Chang, Jianjun Yuan, Chunsong Kang, Youbin Deng, Hui Wang, Baoming Luo, Shenglan Guo, Qi Zhou, Ensheng Xue, Weiwei Zhan, Qing Zhou, Jie Li, Ping Zhou, Man Chen, Ying Gu, Wu Chen, Yuhong Zhang, Jianchu Li, Longfei Cong, Lei Zhu, Hongyan Wang, and Yuxin Jiang
- Subjects
Deep learning ,Ultrasonography ,Breast neoplasms ,Diagnosis ,Artificial intelligence ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
Abstract Background Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model’s ability to assist the radiologists. Methods A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model’s ability to assist the radiologists using two different methods. Results The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p
- Published
- 2022
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