Chinese Medical E-ournals Database

Chinese Journal of Obstetrics & Gynecology and Pediatrics(Electronic Edition) ›› 2020, Vol. 16 ›› Issue (03): 368 -372. doi: 10.3877/cma.j.issn.1673-5250.2020.03.018

Special Issue:

Review

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)

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.

[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] Meifang Zhang, Ying Tan, Qiaozhen Zhu, Xin Wen, Ying Yuan, Yue Qin, Hongbo Guo, Lingxiu Hou, Wenlan Huang, Guiyan Peng, Shengli Li. Artificial intelligence-based quality control of mid-sagittal plane ultrasound images for first trimester fetal crown-rump length[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(09): 945-950.
[2] Yaping Jia, Shu'e Zeng. Ultrasound and pathological characteristics of breast metaplastic carcinoma containing squamous cell carcinoma components[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(08): 844-848.
[3] Wei Tang, Rongquan He, Suning Huang. Application of deep learning in imaging for diagnosis, treatment and prognosis prediction of breast cancer[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 323-328.
[4] Xia Kang, Hao Tian, Jin Qian, Yuan Gao, Hongming Miao, Xiaowei Qi. Osteorin attenuates osteolysis during bone metastasis of cancers through inhibiting osteoclastogenesis[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 329-339.
[5] Xiaoli Yi, Shasha Hu, Yan Zhang. Impact of HER-2 low expression on response of neoadjuvant chemotherapy and prognosis in breast cancer patients[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 340-346.
[6] Jie Shi, Yuntao Li, Haiyan Gao. Prognosis of node-positive luminal A breast cancer patients with neoadjuvant and adjuvant chemotherapy and influencing factors[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 353-361.
[7] Jiaxuan Liu, Binghe Xu. Annual advancement of clinical research on breast cancer in China[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(05): 259-265.
[8] Chengcai Yao, Changchun Liu, Wenjian Huang, Ming Chen. Single-port non-lipolysis fluorescence-guided laparoscopy for axillary lymph node biopsy in early breast cancer[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(05): 266-271.
[9] Bohui Shi, Xiping Ding, Lian Wang, Rong Li, Pingli Guo, Jing Qi, Yao Chen, Na Hao, Yu Ren. Summary of best evidences for prevention and treatment of subcutaneous seroma after breast cancer surgery[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(05): 277-284.
[10] Shuaihua Fan, Wei Guo, Jun Guo. Current situation and prospect of application of decision tree algorithm based on machine learning in prognosis prediction of bloodstream infection[J]. Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Edition), 2023, 17(05): 289-293.
[11] Qingyan Yan, Xiaomei Yong, Hong Luo, Min Du. Prognostic factors and survival analysis of multicenter elderly patients with metastatic breast cancer[J]. Chinese Journal of Operative Procedures of General Surgery(Electronic Edition), 2023, 17(06): 636-638.
[12] Zhiming Li, Chenming Guo, Xiaochen Zhuang, Xueqin Hou, Junxi Gao. Comparative study of qualitative and quantitative indicators of contrast-enhanced ultrasound in early breast cancer[J]. Chinese Journal of Operative Procedures of General Surgery(Electronic Edition), 2023, 17(06): 639-643.
[13] Xiaowei Xing, Yuchen Liu, Bing Zhao, Minggang Wang. A research on predictive value of convolutional neural network based on preoperative abdominal CT for recurrence of incisional hernia after surgical repair[J]. Chinese Journal of Hernia and Abdominal Wall Surgery(Electronic Edition), 2023, 17(06): 677-681.
[14] Manshi Lei, Sisi Deng, Xinrong Wang, Jinbin Huang, Qing Xiang, Anni Xiong, Zhan'ao Meng. Application of artificial intelligence-assisted compression sensing technology in upper abdominal fat-suppressed T2WI sequence[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(05): 551-556.
[15] Bing Han, Jinyang Gu. Research and application prospect of deep learning neural network in diagnosis and treatments for liver cancer[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(05): 480-485.
Viewed
Full text


Abstract