Research status of artificial intelligence in screening and diagnosis of breast cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

CHEN YAO ,  LÜ Qing

Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

LÜ Qing, Email: lqlq1963@163.com

Keywords：

breast cancer; artificial intelligence; neural network; deep learning; review

DOI：

10.7507/1007-9424.201901069

Video：

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Objective To study the application of artificial intelligence based on neural network in breast cancer screening and diagnosis, and to summarize its current situation and clinical application value.Method The combined studies of neural network and artificial intelligence in the directions of breast mammography, breast ultrasound, breast magnetic resonance, and breast pathology diagnosis in CNKI and PubMed database were reviewed.Results Public databases of mammography, such as Digital Database for Screening Mammography (DDSM), provided raw materials for the research of neural network in the field of mammography. Mammography was the most widely used data for screening and diagnosis of breast diseases by neural network. In the field of mammography and color doppler ultrasound, neural network could segment, measure, and analyze the characteristics, judge the benign or malignant, and issue a structured report. The application of neural network in the field of breast ultrasound focused on the diagnosis and treatment of benign and malignant breast diseases. Samsung Madison Group taken the lead in grafting research results into ultrasound instruments. Breast MRI had a lot of high-throughput information, which had became the breakthrough point for the joint study of artificial neural network and imaging omics. Pathological images had more data information to be measured, and quantitative analysis of data was the advantage of neural network. The combination of the two kinds of methods could significantly improve the diagnosis time of pathologists.Conclusions To study the application of artificial intelligence in breast cancer screening and diagnosis is to analyze the application of neural network in breast imaging and pathology. At present, artificial intelligence screening can be used as a physician assistant and an objective diagnostic reference assistant, to improve the diagnosis of breast disease. With the development of medical image histology and neural network, the application of artificial intelligence in medical field can be extended to surgical method design, efficacy evaluation, prognosis analysis, and so on.

Citation： CHEN YAO, LÜ Qing. Research status of artificial intelligence in screening and diagnosis of breast cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2019, 26(5): 625-630. doi: 10.7507/1007-9424.201901069 Copy

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12.	严律南. 人工智能在医学领域应用的现状与展望. 中国普外基础与临床杂志, 2018, 25(5): 7-8.
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14.	Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng, 2013, 15(1): 327-357.
15.	Niklason LT, Christian BT, Niklason LE, et al. Digital tomosynthesis in breast imaging. Radiology, 1997, 205(2): 399-406.
16.	Suckling J, Parker J, Dance DR. The mammographic image analysis society digital mammogram database. [2019-01-30]. http://peipa.essex.ac.uk/info/mias.html.
17.	Samala RK, Chan HP, Hadjiiski LM, et al. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol, 2017, 62(23): 8894-8908.
18.	Samala RK, Chan HP, Hadjiiski L, et al. Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys, 2016, 43(12): 6654.
19.	Lévy D, Jain A. Breast mass classification from mammograms using deep convolutional neural networks. [2019-01-30]. http://xueshu.baidu.com/usercenter/paper/show?paperid=22ef404cd57bcfa680b2eb2bbd63a6bb&site=xueshu_se&hitarticle=1.
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22.	Liberman L, Abramson AF, Squires FB, et al. The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol, 1998, 171(1): 35-40.
23.	Wu W, Wu Z, Yu T, et al. Recent progress on magnetic iron oxide nanoparticles: synthesis, surface functional strategies and biomedical applications. Sci Technol Adv Mater, 2015, 16(2): 023501.
24.	Xu X, Fu L, Chen Y, et al. Breast region segmentation being convolutional neural network in dynamic contrast enhanced MRI. Conf Proc IEEE Eng Med Biol Soc, 2018, 2018: 750-753.
25.	Gallego-Ortiz C, Martel AL. A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions. Med Image Anal, 2019, 51: 116-124.
26.	Perez SM, Binda E, Hayday AC, et al. Human gammadelta T cell responses in breast cancer patients during zoledronate therapy. Immunology, 2010, 131: 115.
27.	Luo J, Ning Z, Zhang S, et al. Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer. Phys Med Biol, 2018, 63(24): 245014.
28.	Kuo SJ, Hsiao YH, Huang YL, et al. Classification of benign and malignant breast tumors using neural networks and three-dimensional power Doppler ultrasound. Ultrasound Obstet Gynecol, 2008, 32(1): 97-102.
29.	Han S, Kang HK, Jeong JY, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol, 2017, 62(19): 7714-7728.
30.	Chiang TC, Huang YS, Chen RT, et al. Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation. IEEE Trans Med Imaging, 2019, 38(1): 240-249.
31.	Cho E, Kim EK, Song MK, et al. Application of computer-aided diagnosis on breast ultrasonography: evaluation of diagnostic performances and agreement of radiologists according to different levels of experience. J Ultrasound Med, 2018, 37(1): 209-216.
32.	Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6: 26286.
33.	Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 2017, 318(22): 2199-2210.
34.	Golden JA. Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen. JAMA, 2017, 318(22): 2184-2186.
35.	Couture HD, Williams LA, Geradts J, et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer, 2018, 4: 30.
36.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. 陈万青, 孙可欣, 郑荣寿, 等. 2014年中国分地区恶性肿瘤发病和死亡分析. 中国肿瘤, 2018, 27(1): 1-14.
3. 中国抗癌协会乳腺癌专业委员会. 中国抗癌协会乳腺癌诊治指南与规范(2017年版). 中国癌症杂志, 2017, 27(9): 695-759.
4. 周志华. 机器学习及其应用. 北京: 清华大学出版社, 2009.
5. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 2014, 1(4): 541-551.
6. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. [2019-01-30]. http://xueshu.baidu.com/usercenter/paper/show?paperid=bfdf67dfdf8cea0c47038f63e91b9df1&site=xueshu_se&hitarticle=1.
7. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. [2019-01-30]. http://xueshu.baidu.com/usercenter/paper/show?paperid=3821a90f58762386e257eb4e6fa11f79&site=xueshu_se&hitarticle=1.
8. Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vision, 2015, 115(3): 211-252.
9. Le QV, Ranzato M, Monga R, et al. Building high-level features using large scale unsupervised learning.[2019-01-30] . http://xueshu.baidu.com/usercenter/paper/show?paperid=b1707e4c8a622ca2c7feff7cd37e6162&site=xueshu_se&hitarticle=1.
10. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
11. 宋彬, 黄子星. 人工智能在影像学的发展、现状及展望. 中国普外基础与临床杂志, 2018, 1(5): 17-21.
12. 严律南. 人工智能在医学领域应用的现状与展望. 中国普外基础与临床杂志, 2018, 25(5): 7-8.
13. Greenspan H, Ginneken BV, Summers RM. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging, 2016, 35(5): 1153-1159.
14. Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng, 2013, 15(1): 327-357.
15. Niklason LT, Christian BT, Niklason LE, et al. Digital tomosynthesis in breast imaging. Radiology, 1997, 205(2): 399-406.
16. Suckling J, Parker J, Dance DR. The mammographic image analysis society digital mammogram database. [2019-01-30]. http://peipa.essex.ac.uk/info/mias.html.
17. Samala RK, Chan HP, Hadjiiski LM, et al. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol, 2017, 62(23): 8894-8908.
18. Samala RK, Chan HP, Hadjiiski L, et al. Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys, 2016, 43(12): 6654.
19. Lévy D, Jain A. Breast mass classification from mammograms using deep convolutional neural networks. [2019-01-30]. http://xueshu.baidu.com/usercenter/paper/show?paperid=22ef404cd57bcfa680b2eb2bbd63a6bb&site=xueshu_se&hitarticle=1.
20. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. [2019-01-30]. http://xueshu.baidu.com/usercenter/paper/show?paperid=eec1bdbec8586e5a6a5f2efc897a4bef&site=xueshu_se&hitarticle=1.
21. Lehman CD, Yala A, Schuster T, et al. Mammographic breast density assessment using deep learning: clinical implementation. Radiology, 2019, 290(1): 52-58.
22. Liberman L, Abramson AF, Squires FB, et al. The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol, 1998, 171(1): 35-40.
23. Wu W, Wu Z, Yu T, et al. Recent progress on magnetic iron oxide nanoparticles: synthesis, surface functional strategies and biomedical applications. Sci Technol Adv Mater, 2015, 16(2): 023501.
24. Xu X, Fu L, Chen Y, et al. Breast region segmentation being convolutional neural network in dynamic contrast enhanced MRI. Conf Proc IEEE Eng Med Biol Soc, 2018, 2018: 750-753.
25. Gallego-Ortiz C, Martel AL. A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions. Med Image Anal, 2019, 51: 116-124.
26. Perez SM, Binda E, Hayday AC, et al. Human gammadelta T cell responses in breast cancer patients during zoledronate therapy. Immunology, 2010, 131: 115.
27. Luo J, Ning Z, Zhang S, et al. Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer. Phys Med Biol, 2018, 63(24): 245014.
28. Kuo SJ, Hsiao YH, Huang YL, et al. Classification of benign and malignant breast tumors using neural networks and three-dimensional power Doppler ultrasound. Ultrasound Obstet Gynecol, 2008, 32(1): 97-102.
29. Han S, Kang HK, Jeong JY, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol, 2017, 62(19): 7714-7728.
30. Chiang TC, Huang YS, Chen RT, et al. Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation. IEEE Trans Med Imaging, 2019, 38(1): 240-249.
31. Cho E, Kim EK, Song MK, et al. Application of computer-aided diagnosis on breast ultrasonography: evaluation of diagnostic performances and agreement of radiologists according to different levels of experience. J Ultrasound Med, 2018, 37(1): 209-216.
32. Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6: 26286.
33. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 2017, 318(22): 2199-2210.
34. Golden JA. Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen. JAMA, 2017, 318(22): 2184-2186.
35. Couture HD, Williams LA, Geradts J, et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer, 2018, 4: 30.
36. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.

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CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Research status of artificial intelligence in screening and diagnosis of breast cancer

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