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Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

作     者:Yuqian Chen Zhen Hua Fan Lin Tiantian Zheng Heng Zhou Shijie Zhang Jing Gao Zhongyi Wang Huafei Shao Wenjuan Li Fengjie Liu Simin Wang Yan Zhang Feng Zhao Hao Liu Haizhu Xie Heng Ma Haicheng Zhang Ning Mao Yuqian Chen;Zhen Hua;Fan Lin;Tiantian Zheng;Heng Zhou;Shijie Zhang;Jing Gao;Zhongyi Wang;Huafei Shao;Wenjuan Li;Fengjie Liu;Simin Wang;Yan Zhang;Feng Zhao;Hao Liu;Haizhu Xie;Heng Ma;Haicheng Zhang;Ning Mao

作者机构:School of Information and Electronic EngineeringShandong Technology and Business UniversityYantai 264005China Department of RadiologyYantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China School of Medical ImagingBinzhou Medical UniversityYantai 264003China Department of RadiologyFudan University Cancer CenterShanghai 200433China Department of RadiologyGuangdong Maternal and Child Health HospitalGuangzhou 510010China School of Computer Science and TechnologyShandong Technology and Business UniversityYantai 264005China Yizhun Medical AI Co.Ltd.Beijing 100080China Big Data and Artificial Intelligence LaboratoryYantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China 

出 版 物:《中国癌症研究:英文版》 (Chinese Journal of Cancer Research)

年 卷 期:2023年第35卷第4期

页      面:408-423页

核心收录:

学科分类:1002[医学-临床医学] 08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 10[医学] 

基  金:supported by the National Natural Science Foundation of China (No.82001775, 82371933) the Natural Science Foundation of Shandong Province of China (No.ZR2021MH120) the Special Fund for Breast Disease Research of Shandong Medical Association (No.YXH2021ZX055) the Taishan Scholar Foundation of Shandong Province of China (No.tsgn202211378)。 

主  题:Deep learning contrast-enhanced mammography breast lesions detection classification 

摘      要:Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.

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