Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study

作者：Tiantian Zheng, Fan Lin, Xianglin Li, Tongpeng Chu, Jing Gao, Shijie Zhang, Ziyin Li, Yajia Gu, Simin Wang, Feng Zhao, Heng Ma, Haizhu Xie, Cong Xu, Haicheng Zhang, Ning Mao · 发表于：EClinicalMedicine · 年份：2023 · DOI：10.1016/j.eclinm.2023.101913 · 被引用次数：46 · 研究领域：AI in cancer detection、Digital Radiography and Breast Imaging、Breast Lesions and Carcinomas

Background: Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. Methods: A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists' reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). Findings: The FA...