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

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

doi:10.1016/j.eclinm.2023.101913

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

EClinicalMedicine. 2023 Mar 17:58:101913. doi: 10.1016/j.eclinm.2023.101913. eCollection 2023 Apr.

Authors

Tiantian Zheng^{1

2}, Fan Lin², Xianglin Li¹, Tongpeng Chu³, Jing Gao², Shijie Zhang², Ziyin Li^{1

2}, Yajia Gu⁴, Simin Wang⁴, Feng Zhao⁵, Heng Ma², Haizhu Xie², Cong Xu⁶, Haicheng Zhang³, Ning Mao^{1

2

7}

Affiliations

¹ School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong, 264003, China.
² Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China.
³ Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China.
⁴ Department of Radiology, Fudan University Cancer Centre, Shanghai, 200433, China.
⁵ School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, 264005, China.
⁶ Physical Examination Centre, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China.
⁷ Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.

Abstract

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 FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916-0.978), 0.940 (95% [CI]: 0.894-0.987) and 0.891 (95% [CI]: 0.816-0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies.

Interpretation: The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability.

Funding: This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).

Keywords: Breast lesions; Classification; Contrast-enhanced mammography; Deep learning; Full automated pipeline system; Segmentation.