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Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

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机构： [1]Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China. [2]Department of Medical Imaging Advanced Research, Beijing Research Institute, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Beijing, China. [3]Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. [4]Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University and Chongqing Key Laboratory of Ultrasound Molecular Imaging, Chongqing, China. [5]Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China. [6]Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China. [7]Department of Ultrasonography, Henan Provincial People's Hospital, Zhengzhou, China. [8]Department of Ultrasound, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China. [9]Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China. [10]Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, China. [11]Department of Ultrasound, The Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [12]Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, China. [13]Department of Medical Ultrasound, The Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, China. [14]Department of Ultrasound, Union Hospital of Fujian Medical University, Fujian Institute of Ultrasound Medicine, Fuzhou, China. [15]Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China. [16]Department of Ultrasonography, Renmin Hospital of Wuhan University, Wuhan, China. [17]Department of Ultrasound, Qilu Hospital, Shandong University, Jinan, 250012, China. [18]Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China. [19]Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [20]Department of Ultrasonography, The Affiliated Hospital of Guizhou Medical University, Guiyang, China. [21]Department of Ultrasound, The First Hospital of Shanxi Medical University, Taiyuan, China. [22]Department of Ultrasound, The Second Hospital of Dalian Medical University, Dalian, China. [23]Department of Medical Imaging Advanced Research, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China

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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.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.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 < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities.The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.© 2022. The Author(s).

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出版当年[2021]版：

大类 | 3 区医学

小类 | 3 区核医学

最新[2025]版：

大类 | 2 区

医学

小类 | 2 区核医学

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出版当年[2020]版：

Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

最新[2023]版：

Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 4.1 最新[2023版] 5.9 最新五年平均 5.231 出版当年[2020版] 4.879 出版当年五年平均 3.579 出版前一年[2019版] 5.036 出版后一年[2021版]

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第一作者机构： [1]Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuai Fu Yuan, Dong Cheng District, Beijing, 100730, China.

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