Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts：a diagnostic study

Authors:
raw_author_name: Yaping Yang
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Ying Zhong
raw_affiliation_string: Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Junwei Li
raw_affiliation_string: Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Jiahao Feng
raw_affiliation_string: Cellsvision (Guangzhou) Medical Technology Inc, China
raw_author_name: Chang Gong
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Yue Hu
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Ran Gu
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Fengtao Liu
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Jingsi Mei
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Xiaofang Jiang
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Jin Wang
raw_affiliation_string: Cellsvision (Guangzhou) Medical Technology Inc, China
raw_author_name: Wei Wu
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
raw_author_name: Herui Yao
raw_affiliation_string: Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China; Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Abstract: We aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts.A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only.The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% confidence interval [95%CI], 0.874~1.000) in the test cohort, and an AUC of 0.906 (95%CI, 0.817~0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone (P<0.05).The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.