Assessing breast disease with deep learning model using bimodal bi-view ultrasound images and clinical information
Fengping Liang,
Yihua Song,
Xiaoping Huang,
Tong Ren,
Qiao Ji,
Yanan Guo,
Xiang Li,
Yajuan Sui,
Xiaohui Xie,
Lanqing Han,
Yuanqing Li,
Yong Ren,
Zuofeng Xu
Affiliations
Fengping Liang
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Yihua Song
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Xiaoping Huang
Department of Ultrasound, Dongguan Songshan Lake Tungwah Hospital, No. 1, Kefa Seventh Road, Songshan Lake Park, Dongguan, China
Tong Ren
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Qiao Ji
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Yanan Guo
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Xiang Li
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Yajuan Sui
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China
Xiaohui Xie
Section of Epidemiology and Population Science, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
Lanqing Han
Center for Artificial Intelligence in Medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
Yuanqing Li
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Research Center for Brain-Computer Interface, Pazhou Lab, Guangzhou, China; Corresponding author
Yong Ren
Artificial Intelligence and Digital Economy Laboratory (Guangzhou), PAZHOU LAB, No.70 Yuean Road, Haizhu District, Guangzhou, China; Shensi Lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China; The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China; Corresponding author
Zuofeng Xu
Department of Medical Ultrasound, The Seventh Affiliated Hospital, Sun Yat-sen University, 628 Zhenyuan Road, Shenzhen, China; Corresponding author
Summary: Breast cancer is the second leading cause of carcinoma-linked death in women. We developed a multi-modal deep-learning model (BreNet) to differentiate breast cancer from benign lesions. BreNet was constructed and trained on 10,108 images from one center and tested on 3,762 images from two centers in three steps. The diagnostic ability of BreNet was first compared with that of six radiologists; a BreNet-aided scheme was constructed to improve the diagnostic ability of the radiologists; and the diagnosis of real-world radiologists’ scheme was then compared with the BreNet-aided scheme. The diagnostic performance of BreNet was superior to that of the radiologists (area under the curve [AUC]: 0.996 vs. 0.841). BreNet-aided scheme increased the pooled AUC of the radiologists from 0.841 to 0.934 for reviewing images, and from 0.892 to 0.934 in the real-world test. The use of BreNet significantly enhances the diagnostic ability of radiologists in the detection of breast cancer.
