Lakhtaryna R. Yu.

THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF BREAST CANCER: BIBLIOMETRIC ANALYSIS

---

Show/Download

About the author:

Lakhtaryna R. Yu.

Heading:

LITERATURE REVIEWS

Type of article:

Scientific article

Annotation:

In modern medicine, artificial intelligence (AI) plays an increasingly important role in diagnosing and treating various pathologies, including oncology. This review examines significant trends in the use of AI to improve the accuracy and speed of breast cancer detection. Thanks to machine learning algorithms and the analysis of large volumes of data, AI systems can analyze digital radiological and pathomorphological images and detect anomalies with high accuracy. The aim of the work: bibliometric analysis and systematization of data on the use of AI algorithms to improve breast cancer diagnosis. Current research shows that AI can significantly reduce the number of false positives and negatives, leading to timely detection of cancer and treatment of patients. In addition, integrating AI into diagnostic practice will allow doctors to receive decision-making support, which will reduce the burden on the diagnostic service and help eliminate the subjectivity factor in routine cases. Therefore, the application of artificial intelligence in diagnosing breast cancer promises to revolutionize approaches to early detection and treatment but requires further research and discussion to ensure safety and efficacy.

Tags:

Bibliography:

1. Nicolis O, De Los Angeles D, Taramasco CA. Contemporary review of breast cancer risk factors and the role of artificial intelligence. Front Oncol. 2024;14:1356014. DOI: 10.3389/fonc.2024.1356014.
2. Mann RM, Athanasiou A, Baltzer PAT, Camps-Herrero J, Clauser P, Fallenberg EM, et al. European Society of Breast Imaging (EUSOBI). Breast cancer screening in women with extremely dense breasts: Recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol. 2022;32(6):4036-4045. DOI: 10.1007/s00330-022-08617-6.
3. Ren W, Chen M, Qiao Y, Zhao F. Global guidelines for breast cancer screening: A systematic review. Breast. 2022;64:85-99. DOI: 10.1016/j. breast.2022.04.003.
4. Mars N, Kerminen S, Tamlander M, Pirinen M, Jakkula E, Aaltonen K, et al. Comprehensive inherited risk estimation for risk-based breast cancer screening in women. J Clin Oncol. 2024;42(13):1477-1487. DOI: 10.1200/JCO.23.00295.
5. Sprague BL, Ichikawa L, Eavey J, Lowry KP, Rauscher G, O’Meara ES, et al. Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone. Cancer. 2023;129(16):2456-2468. DOI: 10.1002/cncr.34768.
6. Li M, Wang H, Qu N, Piao H, Zhu B. Breast cancer screening and early diagnosis in China: a systematic review and meta-analysis on 10.72 million women. BMC Womens Health. 2024;24(1):97. DOI: 10.1186/s12905-024-02924-4.
7. Alsharif WM. The utilization of artificial intelligence applications to improve breast cancer detection and prognosis. Saudi Med J. 2023;44(2):119-127. DOI: 10.15537/smj.2023.44.2.20220611.
8. Larsen M, Olstad CF, Lee CI, Hovda T, Hoff SR, Martiniussen MA, et al. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiol Artif Intell. 2024;6(3):e230375. DOI: 10.1148/ryai.230375.
9. Wilkinson AN, Billette JM, Ellison LF, Killip MA, Islam N, Seely JM. The Impact of Organised Screening Programs on Breast Cancer Stage at Diagnosis for Canadian Women Aged 40-49 and 50-59. Curr Oncol. 2022;29(8):5627-5643. DOI: 10.3390/curroncol29080444.
10. Christiansen SR, Autier P, Støvring H. Change in effectiveness of mammography screening with decreasing breast cancer mortality: a population-based study. Eur J Public Health. 2022;32(4):630-635. DOI: 10.1093/eurpub/ckac047.
11. Song SY, Park B, Hong S, Kim MJ, Lee EH, Jun JK. Comparison of Digital and Screen-Film Mammography for Breast-Cancer Screening: A Systematic Review and Meta-Analysis. J Breast Cancer. 2019;22(2):311-325. DOI: 10.4048/jbc.2019.22.e24.
12. Sprague BL, Kerlikowske K, Bowles EJA, Rauscher GH, Lee CI, Tosteson ANA, et al. Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium. J Natl Cancer Inst. 2019;111(6):629-632. DOI: 10.1093/jnci/djy210.
13. Evans A, Trimboli RM, Athanasiou A, Balleyguier C, Baltzer PA, Bick U, et al. European Society of Breast Imaging (EUSOBI) , with language review by Europa Donna–The European Breast Cancer Coalition. Breast ultrasound: recommendations for information to women and referring physicians by the European Society of Breast Imaging. Insights Imaging. 2018;9(4):449-461. DOI: 10.1007/s13244-018-0636-z.
14. Bick U, Trimboli RM, Athanasiou A, Balleyguier C, Baltzer PAT, Bernathova M, et al. European Society of Breast Imaging (EUSOBI), with language review by Europa Donna–The European Breast Cancer Coalition. Image-guided breast biopsy and localisation: recommendations for information to women and referring physicians by the European Society of Breast Imaging. Insights Imaging. 2020;11(1):12. DOI: 10.1186/s13244-019-0803-x.
15. Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open. 2020;3(3):e200265. DOI: 10.1001/jamanetworkopen.2020.0265. Erratum in: JAMA Netw Open. 2020;3(3):e204429. DOI: 10.1001/jamanetworkopen. 2020.4429.
16. Larsen M, Olstad CF, Lee CI, Hovda T, Hoff SR, Martiniussen MA, et al. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiol Artif Intell. 2024;6(3):e230375. DOI: 10.1148/ryai.230375.
17. Becker AS, Mueller M, Stoffel E, Marcon M, Ghafoor S, Boss A. Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol. 2018;91(1083):20170576. DOI: 10.1259/bjr.20170576.
18. Eskreis-Winkler S, Onishi N, Pinker K, Reiner JS, Kaplan J, Morris EA, et al. Using Deep Learning to Improve Nonsystematic Viewing of Breast Cancer on MRI. J Breast Imaging. 2021;3(2):201-207. DOI: 10.1093/jbi/wbaa102.
19. Yi PH, Singh D, Harvey SC, Hager GD, Mullen LA. Deep CAT: Deep Computer-Aided Triage of Screening Mammography. J Digit Imaging. 2021;34(1):27-35. DOI: 10.1007/s10278-020-00407-0.
20. Ou WC, Polat D, Dogan BE. Deep learning in breast radiology: current progress and future directions. Eur Radiol. 2021;31(7):4872-4885. DOI: 10.1007/s00330-020-07640-9.
21. Choi JS, Han BK, Ko ES, Bae JM, Ko EY, Song SH, et al. Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography. Korean J Radiol. 2019;20(5):749-758. DOI: 10.3348/kjr.2018.0530.
22. Chan HP, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys. 2020;47(5):e218-e227. DOI: 10.1002/mp.13764.
23. Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging. 2020;51(5):1310-1324. DOI: 10.1002/jmri.26878.
24. Adachi M, Fujioka T, Mori M, Kubota K, Kikuchi Y, Xiaotong W, et al. Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images. Diagnostics (Basel). 2020;10(5):330. DOI: 10.3390/diagnostics10050330.
25. Ayatollahi F, Shokouhi SB, Mann RM, Teuwen J. Automatic breast lesion detection in ultrafast DCE-MRI using deep learning. Med Phys. 2021;48(10):5897-5907. DOI: 10.1002/mp.15156.
26. Van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021;31(6):3797-3804. DOI: 10.1007/s00330-021-07892-z.
27. Strohm L, Hehakaya C, Ranschaert ER, Boon WPC, Moors EHM. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol. 2020;30(10):5525-5532. DOI: 10.1007/s00330-020-06946-y.
28. Larsen M, Olstad CF, Lee CI, Hovda T, Hoff SR, Martiniussen MA, et al. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiol Artif Intell. 2024;6(3):e230375. DOI: 10.1148/ryai.230375.

Publication of the article:

«Bulletin of problems biology and medicine», 2024 Issue 4, 175, 46-54 pages, index UDC 618.19-006-071:004.8:001.891

DOI:

10.29254/2077-4214-2024-4-175-46-54