Application of Artificial Intelligence in Breast Medical Imaging Diagnosis

Application of Artificial Intelligence in Breast Medical Imaging Diagnosis

Yao Xiao1*, Yiyuan Qu1, Tingting Wu1

1College of Medicine, China Three Gorges University, Yichang, 443002, China.

American Journal of Cancer Research and Reviews

According to the latest report on urban cancer in China published in 2017: China is one of the countries with the fastest growing incidence of breast cancer, and the age of onset has gradually become younger. Newly diagnosed breast cancer patients in China account for 12.2% of new breast cancer patients worldwide, and the mortality rate is 9.6%. A large amount of clinical experience has proven that the survival rate of breast cancer detected at an early stage is significantly higher than that detected at an advanced stage. Imaging examination is an important method for early detection of breast cancer. With the advent of the Artificial Intelligent, the method of AI + medical imaging has been widely used in lungs, breasts, heart, skull, liver, prostate, bones and other parts. Methods used in breast cancer screening include: breast self-examination and clinical physical examination, mammography of mammography of the breast, ultrasound of the breast, and magnetic resonance imaging (MRI) of the breast. The advantages and disadvantages have been reflected in the development and application in recent years. This article will review the advantages and disadvantages of combined diagnosis of AI and breast medicine. It is hoped that the artificial intelligence of medical imaging screening for breast diseases has a brighter and broader prospect.

Keywords: Breast; Medical imaging; Artificial intelligence (AI); Advantages and disadvantages

Free Full-text PDF

How to cite this article:
Yao Xiao, Yiyuan Qu, Tingting Wu. Application of Artificial Intelligence in Breast Medical Imaging Diagnosis. American Journal of Cancer Research and Reviews, 2020; 4:12. DOI:10.28933/ajocrr-2020-03-1505

1. Shangzhu Bo. Breast cancer prevalence and disease characteristics in China[J]. World’s latest medical information digest, 2017,17(41):253 +256.
2. Chen Wanqing, Zheng Rongshou, Zhang Siwei, Zeng Hongmei, Zou Xiaonong, He Jie.Analysis of Incidence and Death of Malignant Tumors in China in 2013 [J]. China Cancer, 2017,26 (01): 1-7.
3. KIM S H,MI H K,OH K K. Analysis and comparison of breast density according to age on mammogram between Korean and Western women [J]. J Korean Radiolog Society,2000,
4. MELNIKOW J,JOS H,FENTON J,et al.Supplemental screening for breast cancer in women with dense breasts:a systematic review for the U.S. preventive services task force[J].Ann Internal Med,2016,16(4):268⁃278.
5. Jeffers, Abra, Weivasieh, et al. Breast cancer risk and mammographic density assessed with semi automated and sully automated methods and BI-RADS[J]. Rasialogy, 2017, 282(2):348-3 55
6. KOLB T M , LICHY J , NEWHOUSE J H. Comparison of the performance of screening mammography , physical examination , and breast US and evaluation of factors that influ-ence them :an analysis of 27,825 patient evaluations[J]. Radiol,2002,225(1):165⁃175.
7. National Comprehensive Cancer Network. Breast Cancer Screening and Diagnosis [EB/OL]. ( 2018-10-04) [2019-03-19]. /professionals/physiciangls/pdf/breast-screening .pdf
8. HELVIE M A. Digital mammography imaging: breast tomosyn thesis and advanced applica-tions[J]. Radiol Clin North Am, 2010, 48(5): 917-929.
9. Vyborny CJ, Giger ML. Computer vision and artificial intelligence in mammography [J]. AJR Am J Roentgenol, 1994, 162(3):699-708
10. [Parekh V, Jacobs MA. Radiomics:a new application, from established techniques [J] . Expert review of Precision. Medicine and Drug Development, 201 6,1(2 );207-226.
11. Christodoulidis S, Anthimopoulos M, Ebner L, et al. Multisource transfer learning with convolutional neural networks for lung pattern analysis[J]. IEEE J Biomed Health Inform, 2017, 21:76-84.
12. Lee H, Tajmir S, Lee J, et al. Fully automated deep learning system for bone age assess-ment[J]. J Digit Imaging, 2017, 30:427-441.
13. Chan Heang-Ping,Samala Ravi K,Hadjiiski Lubomir M. CAD and AI for breast cancer-recent development and challenges.[J]. The British journal of radiology,2019.
14. CHENG J Z, NI D, CHOU Y H, et al. Computer-aided diagnosis with deep learning architec-architecture: applications to breast lesions in US images and pulmonary nodules in CT scans[J]. Sci Rep, 2016, 6: 24454.
15. Chen Yongzheng, Zhang Enlong, Zhang Jiahui, etc. Advances in the application of multiple arti-ficial intelligence algorithms based on imaging in tumor research[J]. Magnetic Resonance Imaging, 2018, 9 (10): 798-800
16. Fuchsjäger Michael. Is the future of breast imaging with AI?[J]. European radiology, 2019, 29(9).
17. Pisano Etta D. AI shows promise for breast cancer screening.[J]. Nature,2020,577(7788).
18. Mahase Elisabeth. AI system outperforms radiologists in first reading of breast cancer screening, study claims.[J]. BMJ (Clinical re-search ed.),2020,368.
19. Pisano ED, Gatsonis C, Hendrick E, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med 2005;353(17):1773– 1783.
20. Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Med Decis Making 1991;11 (2):88–94.
21. Al-Dhabyani Walid,Gomaa Mohammed,Khaled Hussien,Fahmy Aly. Dataset of breast ultrasound images. [J]. Data in brief,2020,28.
22. Ge-Ge Wu,Li-Qiang Zhou,Jian-Wei Xu,Jia-Yu Wang,Qi Wei,You-Bin Deng,Xin-Wu Cui Christoph F Dietrich.Artificial intelligence in breast ultrasound[J].World Journal of Radiology,2019,11
23. François Destrempes,Isabelle Trop,Louise Al-lard,Boris Chayer,Julian Garcia-Duitama,Mona El Khoury,Lucie Lalonde,Guy Cloutier. Added Value of Quantitative Ultrasound and Machine Learning in BI-RADS 4–5 Assessment of Solid Breast Lesions[J]. Ultrasound in Medicine & Biology,2020,46(2).
24. Yuanming Xiao,Qichang Zhou,Zhiheng Chen. Automated Breast Volume Scanning Versus Conventional Ultrasound in Breast Cancer Screening[J]. Academic Radiology,2015,22(3). 25. 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
26. 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
27. Mahmoud Agha,Ahmed Fathi Eid,Mohamed Nouh. 3T MRI of the breast with computer aided diagnosis, can it help to avoid unnecessary invasive procedures???[J]. Alexandria Journal of Medicine,2016,52(1).
28. 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.
29. Dalm MU, Litjens G, Holland K, et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes[J]. Med Phys, 2017, 44:533-546.
30. Ma Wei, Liu Hongli, Sun Mingjian, Xu Jun, Jiang Yanni.Automatic Segmentation Model of Tumor Region in New Breast Magnetic Resonance Enhanced Images [J] .Chinese Journal of Bio-medical Engineering, 2019,38 (01): 28-34.
31. Tsukada Hiroko,Tsukada Jitsuro,Schrading Simone,Strobel Kevin,Okamoto Takahiro,Kuhl Christiane K. Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy.[J]. Magnetic resonance imaging,2019,62.
32. Perez SM, Binda E, Hayday AC, et al. Human gammadelta T cell responses in breast cancer patients during zoledronate therapy. Immunology, 2010, 131: 115.
33. China artificial intelligence basic data service industry white paper 2019 [C]. IResearch Consulting Research Report (2019 Issue 9): Shanghai iResearch Market Consulting Co., Ltd., 2019: 268-310.