切换至 "中华医学电子期刊资源库"

中华医学超声杂志(电子版) ›› 2024, Vol. 21 ›› Issue (04) : 429 -433. doi: 10.3877/cma.j.issn.1672-6448.2024.04.013

综述

超声在乳腺癌筛查中的应用现状与未来
王琪1, 党晓智2, 许磊3, 宋宏萍2,()   
  1. 1. 710032 西安,空军军医大学西京医院超声医学科;712046 咸阳,陕西中医药大学医学技术学院
    2. 710032 西安,空军军医大学西京医院超声医学科
    3. 710021 西安,西安市中医医院功能科
  • 收稿日期:2024-02-27 出版日期:2024-04-01
  • 通信作者: 宋宏萍
  • 基金资助:
    国家自然科学基金面上项目(82071934); 陕西省科技计划项目国合重点项目(2020KWZ-022); 陕西省高等教育教学改革研究重点项目(21JZ009); 空军军医大学临床研究项目(2021LC2210)

Current status and future perspectives of application of ultrasound in breast cancer screening

Qi Wang, Xiaozhi Dang, Lei Xu   

  • Received:2024-02-27 Published:2024-04-01
引用本文:

王琪, 党晓智, 许磊, 宋宏萍. 超声在乳腺癌筛查中的应用现状与未来[J]. 中华医学超声杂志(电子版), 2024, 21(04): 429-433.

Qi Wang, Xiaozhi Dang, Lei Xu. Current status and future perspectives of application of ultrasound in breast cancer screening[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2024, 21(04): 429-433.

1
郑荣寿, 陈茹, 韩冰峰, 等. 2022年中国恶性肿瘤流行情况分析 [J]. 中华肿瘤杂志, 2024, 46(3): 221-231.
2
沈松杰, 孙强, 黄欣, 等. 中国女性乳腺癌筛查指南(2022年版) [J].中国研究型医院, 2022, 9(2): 6-13.
3
闫慧姣, 王苏蒙, 任文辉, 等. 中国低资源地区女性乳腺癌筛查困境及对策 [J]. 中国公共卫生, 2023, 39(10): 1359-1362.
4
Dan Q, Zheng T, Liu L, et al. Ultrasound for breast cancer screening in resource-limited settings: current practice and future directions[J]. Cancers, 2023, 15(7): 2112.
5
Huppe AI, Inciardi MF, Aripoli AM, et al. Pearls and pitfalls of interpretation of automated breast US [J]. Radiographics, 2023, 43(10): e230023.
6
De Jesus C, Moseley TW, Diaz V, et al. The benefits of screening mammography [J]. Curr Breast Cancer Rep, 2023, 15(2): 103-107.
7
张璐, 韩露, 叶冬熳. X线摄影联合超声检查在乳腺癌筛查中的应用价值 [J]. 中国临床医学影像杂志, 2022, 33(8): 592-596.
8
Ha SM, Chang JM. Breast cancer detection: digital breast tomosynthesis with synthesized mammography versus digital mammography [J]. Radiology, 2023, 309(3): e232911.
9
Sprague BL, Coley RY, Lowry KP, et al. Digital breast tomosynthesis versus digital mammography screening performance on successive screening rounds from the breast cancer surveillance consortium [J]. Radiology, 2023, 307(5): e223142.
10
Houssami N, Zackrisson S, Blazek K, et al. Meta-analysis of prospective studies evaluating breast cancer detection and interval cancer rates for digital breast tomosynthesis versus mammography population screening [J]. Eur J Cancer, 2021, 148: 14-23.
11
Lee SE, Yoon JH, Son NH, et al. Screening in patients with dense breasts: comparison of mammography, artificial intelligence, and supplementary ultrasound [J]. AJR Am J Roentgenol, 2024, 222(1): e2329655.
12
张琦, 宋富桂, 吕哲昊, 等. 致密型乳腺对乳腺癌的影响及其补充筛查 [J]. 放射学实践, 2020, 35(6): 806-809.
13
李逢生, 袁权, 宋灿许, 等. 自动乳腺全容积扫描正交三切面观察乳腺肿瘤边缘征象的应用价值 [J/OL]. 中华医学超声杂志(电子版), 2020, 17(12): 1183-1188.
14
Brem RF, Tabár L, Duffy SW, et al. Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the somoinsight study [J]. Radiology, 2015, 274(3): 663-673.
15
Kuzmiak CM, Ko EY, Tuttle LA, et al. Whole breast ultrasound: comparison of the visibility of suspicious lesions with automated breast volumetric scanning versus hand-held breast ultrasound [J]. Acad Radiol, 2015, 22(7): 870-879.
16
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 Cance, 2022, 13(22): 3145-3151.
17
Wang J, Zheng S, Ding L, et al. Is Ultrasound an accurate alternative for mammography in breast cancer screening in an asian population? a meta-analysis [J]. Diagnostics (Basel), 2020, 10(11): 985.
18
Yuan WH, Hsu HC, Chen YY, et al. Supplemental breast cancer-screening ultrasonography in women with dense breasts: a systematic review and meta-analysis [J]. Br J Cancer, 2020, 123(4): 673-688.
19
Berg WA, Zuley ML, Chang TS, et al. Prospective multicenter diagnostic performance of technologist-performed screening breast ultrasound after tomosynthesis in women with dense breasts (the DBTUST) [J]. J Clin Oncol, 2023, 41(13): 2403-2415.
20
Thigpen D, Kappler A, Brem R. The role of ultrasound in screening dense breasts-a review of the literature and practical solutions for implementation [J]. Diagnostics (Basel), 2018, 8(1): 20.
21
Wong JZY, Chai JH, Yeoh YS, et al. Cost effectiveness analysis of a polygenic risk tailored breast cancer screening programme in Singapore [J]. BMC Health Serv Res, 2021, 21(1): 379.
22
Kerlikowske K, Bissell MCS, Sprague BL, et al. Advanced breast cancer definitions by staging system examined in the breast cancer surveillance consortium [J]. J Natl Cancer Inst, 2021, 113(7): 909-916.
23
魏丽娟, 李利娟, 刘俊田. 间期乳腺癌的研究进展及预防策略 [J]. 肿瘤防治研究, 2021, 48(2): 182-185.
24
Larsen M, Lynge E, Lee CI, et al. Mammographic density and interval cancers in mammographic screening: Moving towards more personalized screening [J]. Breast, 2023, 69: 306-311.
25
Corsetti V, Houssami N, Ghirardi M, et al. Evidence of the effect of adjunct ultrasound screening in women with mammography-negative dense breasts: interval breast cancers at 1 year follow-up [J]. Eur J Cancer, 2011, 47(7): 1021-1026.
26
Niraula S, Biswanger N, Hu P, et al. Incidence, characteristics, and outcomes of interval breast cancers compared with screening-detected breast cancers [J]. JAMA Netw Open, 2020, 3(9): e2018179.
27
Berg WA, Bandos AI, Mendelson EB, et al. Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666 [J]. J Natl Cancer Inst, 2015, 108(4): djv367.
28
Zhang X, Yang L, Liu S, et al. Evaluation of different breast cancer screening strategies for high-risk women in Beijing, China: A real-world population-based study [J]. Front Oncol, 2021, 11: 776848.
29
赵艳霞, 马兰, 连臻强, 等. 2014年中国农村基于超声乳腺癌筛查多中心数据分析 [J]. 中华肿瘤防治杂志, 2020, 27(3): 172-178.
30
Kelly KM, Dean J, Comulada WS, et al. Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts [J]. Eur Radiol, 2010, 20(3): 734-742.
31
Klein Wolterink F, Ab Mumin N, Appelman L, et al. Diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in a clinical screening setting—a retrospective study [J]. Eur Radiol, 2024: 1-10.
32
Choi WJ, Cha JH, Kim HH, et al. Comparison of automated breast volume scanning and hand- held ultrasound in the detection of breast cancer: an analysis of 5,566 patient evaluations [J]. Asian Pac J Cancer Prev, 2014, 15(21): 9101-9105.
33
Niu L, Bao L, Zhu L, et al. Diagnostic performance of automated breast ultrasound in differentiating benign and malignant breast masses in asymptomatic women: a comparison study with handheld ultrasound [J]. J Ultrasound Med, 2019, 38(11): 2871-2880.
34
Ren W, Yan H, Zhao X, et al. Integration of handheld ultrasound or automated breast ultrasound among women with negative mammographic screening findings: a multi-center population-based study in China [J]. Acad Radiol, 2023, 30Suppl 2: S114-S126.
35
Kim SM, Jang M, Yun B, et al. Automated versus handheld breast ultrasound for evaluating axillary lymph nodes in patients with breast cancer [J]. Korean J Radiol, 2024, 25(2): 146-156.
36
Han S, Kang HK, Jeong JY, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images [J]. Phys Med Biol, 2017, 62(19): 7714-7728.
37
Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography [J]. Ultrasonics, 2016, 72: 150-157.
38
Singh VK, Abdel-Nasser M, Akram F, et al. Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework [J]. Expert Syst Appl, 2020, 162: 113870.
39
Lee J, Kang BJ, Kim SH, et al. Evaluation of computer-aided detection (CAD) in screening automated breast ultrasound based on characteristics of CAD marks and false-positive marks [J]. Diagnostics (Basel), 2022, 12(3): 583.
40
Yang S, Gao X, Liu L, et al. Performance and reading time of automated breast US with or without computer-aided detection [J]. Radiology, 2019, 292(3): 540-549.
41
Hejduk P, Marcon M, Unkelbach J, et al. Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network [J]. Eur Radiol, 2022, 32(7): 4868-4878.
42
Wang Z, He B, Zhang Y, et al. Design and implementation for portable ultrasound-aided breast cancer screening system [J]. J Biomed Eng, 2022: 39(2): 390-397.
43
Love SM, Berg WA, Podilchuk C, et al. Palpable breast lump triage by minimally trained operators in Mexico using computer-assisted diagnosis and low-cost ultrasound [J]. J Glob Oncol, 2018, 4: 1-9.
44
Berg WA, López Aldrete AL, Jairaj A, et al. Toward AI-supported US triage of women with palpable breast lumps in a low-resource setting [J]. Radiology, 2023, 307(4): e223351.
No related articles found!
阅读次数
全文


摘要