切换至 "中华医学电子期刊资源库"
高级检索
中华妇幼临床医学杂志(电子版)

中华妇幼临床医学杂志(电子版) ›› 2020, Vol. 16 ›› Issue (03) : 368 -372. doi: 10.3877/cma.j.issn.1673-5250.2020.03.018

所属专题： 文献；

综述

人工智能乳腺超声对乳腺癌的诊断及预后预测价值
谢川博1, 满琴2, 罗红3,†()   
  1. 1. 四川大学华西第二医院超声科、出生缺陷与相关妇儿疾病教育部重点实验室，成都　610041；自贡市妇幼保健院超声科，四川　643000
    2. 自贡市妇幼保健院产前诊断中心，四川　643000
    3. 四川大学华西第二医院超声科、出生缺陷与相关妇儿疾病教育部重点实验室，成都　610041
  • 收稿日期:2020-01-03 修回日期:2020-05-05 出版日期:2020-06-01
  • 通信作者: 罗红

Values of artificial intelligence breast ultrasound in diagnosis and prognosis assessment of breast carcinoma

Chuanbo Xie1, Qin Man2, Hong Luo3,†()   

  1. 1. Department of Ultrasonography, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China; Department of Ultrasonography, Zigong Hospital of Women and Children Heath Care, Zigong 643000, Sichuan Province, China
    2. Prenatal Diagnosis Center, Zigong Hospital of Women and Children Heath Care, Zigong 643000, Sichuan Province, China
    3. Department of Ultrasonography, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
  • Received:2020-01-03 Revised:2020-05-05 Published:2020-06-01
  • Corresponding author: Hong Luo
  • About author:
    Corresponding author: Luo Hong, Email:
  • Supported by:
    National Key Research & Development Project(2017YFC0113905)
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

谢川博, 满琴, 罗红. 人工智能乳腺超声对乳腺癌的诊断及预后预测价值[J]. 中华妇幼临床医学杂志(电子版), 2020, 16(03): 368-372.

Chuanbo Xie, Qin Man, Hong Luo. Values of artificial intelligence breast ultrasound in diagnosis and prognosis assessment of breast carcinoma[J]. Chinese Journal of Obstetrics & Gynecology and Pediatrics(Electronic Edition), 2020, 16(03): 368-372.

人工智能(AI)乳腺超声将AI技术应用于乳腺癌的诊断及预后预测，不仅可以为超声科医师节省时间，还可以弥补由于初学者经验和技能不足导致的误诊及漏诊。现代医学影像学是AI在临床中发挥重要作用的最早领域之一。AI乳腺超声采集乳腺超声图像(BUI)，作为一种横断面成像技术，应用计算机辅助设计(CAD)系统，对乳腺癌进行计算机辅助诊断，可提高临床对乳腺癌诊断的准确性。目前，CAD系统可帮助超声科医师更有效地实现对乳腺癌的早期筛查。AI乳腺超声可对乳腺癌病灶进行自动识别及分类，甚至模拟临床医师对乳腺癌进行诊断和预后评估。笔者拟就AI乳腺超声对乳腺癌的诊断及预后预测价值的最新研究进展，进行阐述。

关键词: 乳腺肿瘤, 超声检查，乳房, 人工智能, 诊断，计算机辅助, 图像处理，计算机辅助, 预后评估, 女(雌)性

The application of artificial intelligence (AI) breast ultrasonography for diagnosis breast carcinoma and prognosis prediction of breast cancer treatment can not only save time for ultrasound doctors, but also reduce the misdiagnosis and miss diagnosis cause by the lack of experience and skills of beginners. Modern medical imaging is one of the earliest areas where AI plays an important role in clinic. As a cross-sectional imaging technology, breast ultrasound image (BUI) collected by AI breast ultrasonography uses a computer-aided design (CAD) system to perform computer-aided diagnosis of breast cancer, and it can improve the accuracy of clinical diagnosis of breast cancer. Up to now, the intelligent CAD system can help ultrasound doctors more effectively screening early breast cancer. AI breast ultrasound can automatically identify and classify breast cancer lesions, and even simulate clinicians in diagnosis and prognosis prediction of patients with breast cancer. This article focuses on the latest research progresses of diagnosis and prognosis prediction of breast cancer by AI breast ultrasound.

Key words: Breast neoplasms, Ultrasonography, mammary, Artificial intelligence, Diagnosis, computer-assisted, Image processing, computer-assisted, Prognosis assessment, Female
[1]
Siegel R, DeSantis C, Jemal A. Colorectal cancer statistics, 2014 [J]. CA Cancer J Clin, 2014, 64(2): 104-117. DOI: 10.3322/caac.21220.
[2]
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012 [J]. Int J Cancer, 2015, 136(5): E359-E386. DOI: 10.1002/ijc.29210.
[3]
Wu GG, Zhou LQ, Xu JW, et al. Artificial intelligence in breast ultrasound [J]. World J Radiol, 2019, 11(2): 19. DOI: 10.4329/wjr.v11.i2.19.
[4]
Sepandi M, Taghdir M, Rezaianzadeh A, et al. Assessing breast cancer risk with an artificial neural network [J]. Asian Pac J Cancer Prev, 2018, 19(4): 1017. DOI: 10.22034/APJCP.2018.19.4.1017.
[5]
Zain NM, Chelliah KK. Breast imaging using electrical impedance tomography: correlation of quantitative assessment with visual interpretation [J]. Asian Pac J Cancer Prev, 2014, 15(3): 1327-1331. DOI: 10.7314/APJCP.2014.15.3.1327.
[6]
Shen WC, Chang RF, Moon WK, et al. Breast ultrasound computer-aided diagnosis using BI-RADS features [J]. Acad Radiol, 2007, 14(8): 928-939. DOI: 10.1016/j.acra.2007.04.016.
[7]
EI-Naqa I, Yang Y, Wernick MN, et al. A support vector machine approach for detection of microcalcifications [J]. IEEE Trans Med Imaging, 2002, 21(12): 1552-1563. DOI: 10.1109/TMI.2002.806569.
[8]
Marmot MG, Altman DG, Cameron DA, et al. The benefits and harms of breast cancer screening: an independent review [J]. Br J Cancer, 2013, 108(11): 2205-2240. DOI: 10.1038/bjc.2013.177.
[9]
Ciritsis A, Rossi C, Eberhard M, et al. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making [J]. Eur Radiol, 2019, 29(10): 5458-5468. DOI: 10.1007/s00330-019-06118-7.
[10]
Fujioka T, Kubota K, Mori M, et al. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network [J]. Jap J Radiol, 2019, 37(6): 466-472. DOI: 10.1007/s11604-019-00831-5.
[11]
吴英，罗良平，许波，等. 基于迁移学习的乳腺肿瘤超声图像智能分类诊断[J]. 中国医学影像技术，2019, 35(3): 42-45. DOI: 10.13929/j.1003-3289.201807052.
[12]
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. DOI: 10.1088/1361-6560/aa82ec.
[13]
Lo CM, Chang RF. Intelligent diagnosis of breast cancer based on quantitative B-mode and elastography features//Artificial intelligence in decision support systems for diagnosis in medical imaging [M]. Switzerland: Springer, Cham, 2018: 165-191. DOI: 10.1007/978-3-319-68843-5.
[14]
Moon WK, Chang SC, Huang CS, et al. Breast tumor classification using fuzzy clustering for breast elastography [J]. Ultrasound Med Biol, 2011, 37(5): 700-708. DOI: 10.1016/j.ultrasmedbio.2011.02.003.
[15]
Chang SC, Lai YC, Chou YH, et al. Breast elastography diagnosis based on dynamic sequence features [J]. Med Phys, 2013, 40(2): 022905. DOI: 10.1118/1.4788652.
[16]
Marcomini K, Fleury E, Oliveira V, et al. Evaluation of a computer-aided diagnosis system in the classification of lesions in breast strain elastography imaging [J]. Bioengineering (Basel), 2018, 5(3): 62. DOI: 10.3390/bioengineering5030062.
[17]
Zhang Q, Song S, Xiao Y, et al. Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks [J]. Med Eng Phys, 2019, 64(1): 1-6. DOI: 10.1016/j.medengphy.2018.12.005.
[18]
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. DOI: 10.1007/s00330-009-1588-y.
[19]
Moon WK, Shen YW, Huang CS, et al. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images [J]. Ultrasound Med Biol, 2011, 37(4): 539-548. DOI: 10.1016/j.ultrasmedbio.2011.01.006.
[20]
Lo C, Shen YW, Huang CS, et al. Computer-aided multiview tumor detection for automated whole breast ultrasound [J]. Ultrason Imaging, 2014, 36(1): 3-17. DOI: 10.1177/0161734613507240.
[21]
Chiang TC, Huang YS, Chen RT, et al. Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation [J]. IEEE Trans Med Imaging, 2019, 38(1): 240-249. DOI: 10.1109/tmi.2018.2860257.
[22]
Tran WT, Jerzak K, Lu FI, et al. Personalized breast cancer treatments using artificial intelligence in radiomics and pathomics [J]. J Med Imaging Radiat Sci, 2019, 50(4 Suppl 2): S32-S41. DOI: 10.1016/j.jmir.2019.07.010.
[23]
Nasief HG, Rosado-Mendez IM, Zagzebski JA, et al. A quantitative ultrasound-based multi-parameter classifier for breast masses [J]. Ultrasound Med Biol, 2019, 45(7): 1603-1616. DOI: 10.1016/j.ultrasmedbio.2019.02.025.
[24]
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data [J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
[25]
Klimonda Z, Karwat P, Dobruch-Sobczak K, et al. Breast-lesions characterization using quantitative ultrasound features of peritumoral tissue [J]. Sci Rep, 2019, 9(1): 7963. DOI: 10.1038/s41598-019-44376-z.
[26]
Sannachi L, Gangeh M, Tadayyon H, et al. Breast cancer treatment response monitoring using quantitative ultrasound and texture analysis: comparative analysis of analytical models [J]. Transl Oncol, 2019, 12(10): 1271-1281. DOI: 10.1016/j.tranon.2019.06.004.
[27]
Hsu SM, Kuo WH, Kuo FC, et al. Breast tumor classification using different features of quantitative ultrasound parametric images [J]. Int J Comput Assist Radiol Surg, 2019, 14(4): 623-633. DOI: 10.1007/s11548-018-01908-8.
[28]
Sadeghi-Naini A, Sannachi L, Pritchard K, et al. Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture [J]. Oncotarget, 2014, 5(11): 3497-3511. DOI: 10.18632/oncotarget.1950.
[29]
Tadayyon H, Sadeghi-Naini A, Sannachi L, et al. Quantitative ultrasound assessment of tumor responses to chemotherapy using a time-integrated multi-parameter approach [J]. J Acoust Soc Am, 2014, 136(4): 2123-2123. DOI: 10.1121/1.4899647.
[30]
Lassau N, Estienne T, de Vomecourt P, et al. Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI [J]. Diagn Intervent Imaging, 2019, 100(4): 199-209. DOI: 10.1016/j.diii.2019.02.001.
[1] 张梅芳, 谭莹, 朱巧珍, 温昕, 袁鹰, 秦越, 郭洪波, 侯伶秀, 黄文兰, 彭桂艳, 李胜利. 早孕期胎儿头臀长正中矢状切面超声图像的人工智能质控研究[J]. 中华医学超声杂志（电子版）, 2023, 20(09): 945-950.
[2] 郏亚平, 曾书娥. 含鳞状细胞癌成分的乳腺化生性癌的超声与病理特征分析[J]. 中华医学超声杂志（电子版）, 2023, 20(08): 844-848.
[3] 唐玮, 何融泉, 黄素宁. 深度学习在乳腺癌影像诊疗和预后预测中的应用[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 323-328.
[4] 康夏, 田浩, 钱进, 高源, 缪洪明, 齐晓伟. 骨织素抑制破骨细胞分化改善肿瘤骨转移中骨溶解的机制研究[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 329-339.
[5] 衣晓丽, 胡沙沙, 张彦. HER-2低表达对乳腺癌新辅助治疗疗效及预后的影响[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 340-346.
[6] 施杰, 李云涛, 高海燕. 腋窝淋巴结阳性Luminal A型乳腺癌患者新辅助与辅助化疗的预后及影响因素分析[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 353-361.
[7] 伍秋苑, 陈佩贤, 邓裕华, 何添成, 周丹. 肠道微生物在乳腺癌中的研究进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 362-365.
[8] 谭巧, 苏小涵, 侯令密, 黎君彦, 邓世山. 乳腺髓样癌的诊治进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 366-368.
[9] 周婉丽, 钱铮, 李喆. 槐耳在乳腺癌免疫治疗中的研究进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 369-371.
[10] 熊倩, 罗凤. 乳腺癌患者术后康复现状与对策的研究进展[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 372-374.
[11] 杨小菁, 姜瑞瑞, 石玉香, 王静静, 李长天. 乳腺孤立性纤维性肿瘤一例[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 375-377.
[12] 冯雪园, 韩萌萌, 马宁. 乳腺原发上皮样血管内皮瘤一例[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 378-380.
[13] 晏晴艳, 雍晓梅, 罗洪, 杜敏. 成都地区老年转移性乳腺癌的预后及生存因素研究[J]. 中华普外科手术学杂志(电子版), 2023, 17(06): 636-638.
[14] 李智铭, 郭晨明, 庄晓晨, 候雪琴, 高军喜. 早期乳腺癌超声造影定性及定量指标的对比研究[J]. 中华普外科手术学杂志(电子版), 2023, 17(06): 639-643.
[15] 李晓阳, 刘柏隆, 周祥福. 大数据及人工智能对女性盆底功能障碍性疾病的诊断及风险预测[J]. 中华腔镜泌尿外科杂志(电子版), 2023, 17(06): 549-552.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号