Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study

Authors:
raw_author_name: Jing Chen
raw_affiliation_string: Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China.
raw_author_name: Zhibin Huang
raw_affiliation_string: Jinan University, Guangzhou, Guangdong, China.
raw_author_name: Yitao Jiang
raw_affiliation_string: Research and development department, Illuminate, LLC, Shenzhen, Guangdong, China.
raw_author_name: Huaiyu Wu
raw_affiliation_string: Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China.
raw_author_name: Hongtian Tian
raw_affiliation_string: Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China.
raw_author_name: Chen Cui
raw_affiliation_string: Research and development department, Illuminate, LLC, Shenzhen, Guangdong, China.
raw_author_name: Siyuan Shi
raw_affiliation_string: Research and development department, Illuminate, LLC, Shenzhen, Guangdong, China.
raw_author_name: Shuzhen Tang
raw_affiliation_string: Jinan University, Guangzhou, Guangdong, China.
raw_author_name: Jinfeng Xu
raw_affiliation_string: Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China.
raw_author_name: Dong Xu
raw_affiliation_string: Institute of Basic Medicine and Cancer (IBMC), The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
raw_author_name: Fajin Dong
raw_affiliation_string: Ultrasound Department, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, Guangdong, China; Jinan University, Guangzhou, Guangdong, China. Electronic address: dongfajin@szhospital.com.

Abstract: Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening.Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts.This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening.The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts.