Preliminary application and agreement analysis of the DeepBC intelligent breast ultrasound diagnostic system in primary-level breast cancer screening_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

HE Tao ^1,2 , CHEN Maoshan ³ , LIAO Minghua ⁴ , JIANG Chuan ⁵ ,  Lü Qing ²

1. Day Surgery Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Breast Disease Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. Department of Thyroid and Breast Surgery, Suining Central Hospital, Suining, Sichuan 629000, P. R. China;
4. Department of Ultrasound, Shifang Maternal and Child Health Hospital, Shifang, Sichuan 618400, P. R. China;
5. Department of Ultrasound, Wanzhou Maternal and Child Health Hospital, Wanzhou District, Chongqing 404100, P. R. China;

Corresponding?author：

Lü Qing, Email: lvqing@wchscu.cn

Keywords：

breast cancer; screening; ultrasound; intelligent diagnosis; DeepBC

DOI：

10.7507/1007-9424.202606067

Video：

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Objective To analyze the results of the preliminary application of the breast cancer ultrasound intelligent diagnostic system (DeepBC) in primary-level breast cancer screening. Methods Women who underwent breast ultrasound screening at Shifang Maternal and Child Health Hospital in Sichuan Province and at Wanzhou Maternal and Child Health Hospital in Chongqing from January 2023 to August 2024 were retrospectively included. DeepBC performed an independent risk assessment using static breast ultrasound (BUS) lesion images acquired by sonographers and did not independently detect lesions. The distributions of Breast Imaging Reporting and Data System (BI-RADS) categories and risk-stratification results between DeepBC and sonologists were compared. BI-RADS categories 1–3 were defined as low risk, category 4 or higher as high risk, and category 0 or indeterminate results as uncertain. The agreement rate and patterns of risk upgrading and downgrading between DeepBC and sonographers were evaluated. Results A total of 16 750 women were included, and BUS detected 6 812 breast lesions in 4 496 participants. Sonologists classified 5 932 lesions (87.08%) as low risk, 863 (12.67%) as high risk, and 17 (0.25%) as uncertain; the corresponding numbers classified by DeepBC were 2 517 (36.95%), 4 104 (60.25%), and 191 (2.80%), respectively. Three-category agreement analysis of all 6 812 lesions yielded a Cohen Kappa of 0.135 [95%CI (0.115, 0.155)], with an overall agreement rate of 47.94% (3 266/6 812). Of the 5 932 lesions initially classified as low-risk by sonologist, DeepBC upgraded 3 308 (55.77%) to high-risk. Conversely, among the 863 high-risk lesions classified by sonologists, DeepBC concurred on 791, yielding a high-risk agreement rate of 91.66%. In a subgroup of 3 781 hypoechoic lesions, a three-class agreement analysis was performed. The Cohen Kappa between the DeepBC and sonologists was 0.215 [95%CI (0.188, 0.242)], with an overall agreement rate of 53.45% (2 021/3 781). Specifically, within this subgroup, DeepBC upgraded 1 636 (54.95%) of the 2 977 sonologist-defined low-risk lesions, and for the 798 lesions originally deemed high-risk by sonologists, DeepBC matched 740, corresponding to a concordance rate of 92.73%. Conclusions DeepBC shows a high concordance with BUS performed by sonologists in the classification of high-risk lesions; however, the overall agreement between the two in risk stratification is low, and DeepBC frequently upgrades lesions classified as low risk by sonologists. Because complete histopathological or long-term imaging reference standards were unavailable in this study, these findings primarily reflect differences in risk stratification between the two approaches and cannot be interpreted as evidence of the diagnostic accuracy of DeepBC. Further studies incorporating histopathological confirmation, standardized imaging follow-up, and multicenter external validation are needed to evaluate its clinical value.

Citation： HE Tao, CHEN Maoshan, LIAO Minghua, JIANG Chuan, Lü Qing. Preliminary application and agreement analysis of the DeepBC intelligent breast ultrasound diagnostic system in primary-level breast cancer screening. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(9): 1220-1227. doi: 10.7507/1007-9424.202606067 Copy

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6.	Shen S, Zhou Y, Xu Y, et al. A multi-centre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women [J]. Br J Cancer, 2015, 112(6): 998-1004.
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12.	Li H, Zhao J, Jiang Z. Deep learning-based computer-aided detection of ultrasound in breast cancer diagnosis: a systematic review and meta-analysis [J/OL]. Clin Radiol, 2024, 79(11): e1403-e1413. doi: 10.1016/j.crad.2024.08.002.
13.	Bunnell A, Valdez D, Strand F, et al. Artificial intelligence-enhanced handheld breast ultrasound for screening: a systematic review of diagnostic test accuracy [J/OL]. PLOS Digit Health, 2025, 4(9): e0001019. doi: 10.1371/journal.pdig.0001019.
14.	Shen J, Liu Y, Liu A, et al. Artificial intelligence-assisted ultrasound screening for breast cancer in China: a prospective, clustered, controlled, population-based study [J/OL]. Breast Cancer Res, 2025, 27(1): 173. doi: 10.1186/s13058-025-02128-0.
15.	Qi X, Zhang L, Chen Y, et al. Automated diagnosis of breast ultrasonography images using deep neural networks [J]. Med Image Anal, 2019, 52: 185-198.
16.	D’Orsi CJ, Sickles EA, Mendelson EB, et al. ACR BI-RADS atlas, breast imaging reporting and data system [M]. Reston, VA: American College of Radiology, 2013.
17.	Moon HJ, Kim MJ, Yoon JH, et al. Follow-up interval for probably benign breast lesions on screening ultrasound in women at average risk for breast cancer with dense breasts [J]. Acta Radiol, 2018, 59(9): 1045-1050.
18.	Huh S, Suh HJ, Kim EK, et al. Follow-up intervals for breast imaging reporting and data system category 3 lesions on screening ultrasound in screening and tertiary referral centers [J]. Korean J Radiol, 2020, 21(9): 1027-1035.
19.	Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022 [J]. J Natl Cancer Cent, 2024, 4(1): 47-53.
20.	Li M, Wang H, Qu N, et al. Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women [J/OL]. BMC Womens Health, 2024, 24(1): 97. doi: 10.1186/s12905-024-02924-4.
21.	Xu H, Xu B. Epidemiology, early detection, and management of breast cancer in China: a comprehensive review [J]. Chin J Cancer Res, 2025, 37(6): 882-899.
22.	Song H, Moon WK. Breast cancer screening programs in China: implementation, challenges, and future perspectives [J]. Korean J Radiol, 2026, 27(5): 406-418.
23.	Xu HF, Wang H, Liu Y, et al. Baseline performance of ultrasound-based strategies in breast cancer screening among Chinese women [J]. Acad Radiol, 2024, 31(12): 4772-4779.
24.	王鑫, 李燕婕, 雷林, 等. 中國適齡女性乳腺癌篩查服務的可及性—篩查率及其構成分析 [J]. 中華流行病學雜志, 2023, 44(8): 1302-1308.
25.	Gao L, Li J, Gu Y, et al. Breast ultrasound in Chinese hospitals: a cross-sectional study of the current status and influencing factors of BI-RADS utilization and diagnostic accuracy [J/OL]. Lancet Reg Health West Pac, 2022, 29: 100576. doi: 10.1016/j.lanwpc.2022.100576.
26.	Yan H, Wang Q, Dang L, et al. Implementation and maintenance of breast cancer screening among Chinese rural women: a mixed-methods evaluation based on RE-AIM framework [J/OL]. BMC Public Health, 2025, 25(1): 2502. doi: 10.1186/s12889-025-23679-z.

1. Sung H, Filho AM, Laversanne M, et al. Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries [J/OL]. CA Cancer J Clin, 2026, 76(4): e70090. doi: 10.3322/caac.70090.
2. Benitez Fuentes JD, Morgan E, de Luna Aguilar A, et al. Global stage distribution of breast cancer at diagnosis: a systematic review and meta-analysis [J]. JAMA Oncol, 2024, 10(1): 71-78.
3. Li J, Zhou J, Wang H, et al. Trends in disparities and transitions of treatment in patients with early breast cancer in China and the US, 2011 to 2021 [J/OL]. JAMA Netw Open, 2023, 6(6): e2321388. doi: 10.1001/jamanetworkopen.2023.21388.
4. Ding R, Xiao Y, Mo M, et al. Breast cancer screening and early diagnosis in Chinese women [J]. Cancer Biol Med, 2022, 19(4): 450-467.
5. Li T, Li J, Heard R, et al. Understanding mammographic breast density profile in China: a Sino-Australian comparative study of breast density using real-world data from cancer screening programs [J]. Asia Pac J Clin Oncol, 2022, 18(6): 696-705.
6. Shen S, Zhou Y, Xu Y, et al. A multi-centre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women [J]. Br J Cancer, 2015, 112(6): 998-1004.
7. Wang Y, Li Y, Song Y, et al. Comparison of ultrasound and mammography for early diagnosis of breast cancer among Chinese women with suspected breast lesions: a prospective trial [J]. Thorac Cancer, 2022, 13(22): 3145-3151.
8. Bahl M, Chang JM, Mullen LA, et al. Artificial intelligence for breast ultrasound: AJR expert panel narrative review [J/OL]. AJR Am J Roentgenol, 2024, 223(6): e2330645. doi: 10.2214/ajr.23.30645.
9. Huang Y, Dai H, Song F, et al. Preliminary effectiveness of breast cancer screening among 1.22 million Chinese females and different cancer patterns between urban and rural women [J/OL]. Sci Rep, 2016, 6: 39459. doi: 10.1038/srep39459.
10. Zeng H, Wu S, Ma F, et al. Disparities in stage at diagnosis among breast cancer molecular subtypes in China [J]. Cancer Med, 2023, 12(9): 10865-10876.
11. Lawrence R, Dodsworth E, Massou E, et al. Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review [J/OL]. EClinicalMedicine, 2025, 83: 103228. doi: 10.1016/j.eclinm.2025.103228.
12. Li H, Zhao J, Jiang Z. Deep learning-based computer-aided detection of ultrasound in breast cancer diagnosis: a systematic review and meta-analysis [J/OL]. Clin Radiol, 2024, 79(11): e1403-e1413. doi: 10.1016/j.crad.2024.08.002.
13. Bunnell A, Valdez D, Strand F, et al. Artificial intelligence-enhanced handheld breast ultrasound for screening: a systematic review of diagnostic test accuracy [J/OL]. PLOS Digit Health, 2025, 4(9): e0001019. doi: 10.1371/journal.pdig.0001019.
14. Shen J, Liu Y, Liu A, et al. Artificial intelligence-assisted ultrasound screening for breast cancer in China: a prospective, clustered, controlled, population-based study [J/OL]. Breast Cancer Res, 2025, 27(1): 173. doi: 10.1186/s13058-025-02128-0.
15. Qi X, Zhang L, Chen Y, et al. Automated diagnosis of breast ultrasonography images using deep neural networks [J]. Med Image Anal, 2019, 52: 185-198.
16. D’Orsi CJ, Sickles EA, Mendelson EB, et al. ACR BI-RADS atlas, breast imaging reporting and data system [M]. Reston, VA: American College of Radiology, 2013.
17. Moon HJ, Kim MJ, Yoon JH, et al. Follow-up interval for probably benign breast lesions on screening ultrasound in women at average risk for breast cancer with dense breasts [J]. Acta Radiol, 2018, 59(9): 1045-1050.
18. Huh S, Suh HJ, Kim EK, et al. Follow-up intervals for breast imaging reporting and data system category 3 lesions on screening ultrasound in screening and tertiary referral centers [J]. Korean J Radiol, 2020, 21(9): 1027-1035.
19. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022 [J]. J Natl Cancer Cent, 2024, 4(1): 47-53.
20. Li M, Wang H, Qu N, et al. Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women [J/OL]. BMC Womens Health, 2024, 24(1): 97. doi: 10.1186/s12905-024-02924-4.
21. Xu H, Xu B. Epidemiology, early detection, and management of breast cancer in China: a comprehensive review [J]. Chin J Cancer Res, 2025, 37(6): 882-899.
22. Song H, Moon WK. Breast cancer screening programs in China: implementation, challenges, and future perspectives [J]. Korean J Radiol, 2026, 27(5): 406-418.
23. Xu HF, Wang H, Liu Y, et al. Baseline performance of ultrasound-based strategies in breast cancer screening among Chinese women [J]. Acad Radiol, 2024, 31(12): 4772-4779.
24. 王鑫, 李燕婕, 雷林, 等. 中國適齡女性乳腺癌篩查服務的可及性—篩查率及其構成分析 [J]. 中華流行病學雜志, 2023, 44(8): 1302-1308.
25. Gao L, Li J, Gu Y, et al. Breast ultrasound in Chinese hospitals: a cross-sectional study of the current status and influencing factors of BI-RADS utilization and diagnostic accuracy [J/OL]. Lancet Reg Health West Pac, 2022, 29: 100576. doi: 10.1016/j.lanwpc.2022.100576.
26. Yan H, Wang Q, Dang L, et al. Implementation and maintenance of breast cancer screening among Chinese rural women: a mixed-methods evaluation based on RE-AIM framework [J/OL]. BMC Public Health, 2025, 25(1): 2502. doi: 10.1186/s12889-025-23679-z.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Preliminary application and agreement analysis of the DeepBC intelligent breast ultrasound diagnostic system in primary-level breast cancer screening

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