Now and Future Use of Artificial Intelligence (Ai) in the Management of Breast Cancer

Breast cancer (BC) remains a major etiology of motility for female gender in the world, accounting for more than 2.3 million new fatalities annually. Despite early detection and treatment advancements, significant challenges persist, particularly in personalized management and preventive interventions. Recent technological innovations, such as the integration of artificial intelligence (AI), have shown promise in improving BC diagnosis and management. AI has been successfully applied in mammography, MRI, and advanced imaging techniques to enhance the accuracy and efficiency of BC detection, offering earlier intervention and personalized care. AI's ability to analyze high-resolution images has proven effective in detecting breast masses, classifying benign and malignant tumors, and assessing breast cancer risk. Additionally, deep learning (DL) algorithms and radiomics have emerged as valuable tools, providing refined predictive analytics and reducing false-positive rates. However, the widespread adoption of AI in clinical settings is hindered by factors like data heterogeneity, confidentiality concerns, limited generalizability, and the need for specialized training among healthcare professionals. Despite these limitations, AI’s potential in revolutionizing breast cancer care is immense, providing appreciable development in the are of disease screening accuracy and treatment outcomes. Addressing the existing challenges through collaborative research, standardized data, and ethical frameworks is crucial to realizing the full benefits of AI in BC diagnosis and management.

Artificial Intelligence, MRI, Breast Cancer, Deep learning (DL), radiomics, Diagnosis

1. Nicolis, O., De Los Angeles, D., & Taramasco, C. (2024). A contemporary review of breast cancer risk factors and the role of artificial intelligence. Frontiers in Oncology, 14, 1356014. https://doi.org/10.3389/fonc.2024.1356014 [Google Scholar] [Crossref]

2. Uzun O. D., Ikechukwu E. D., Uzun, B., & Ozsahin, I. (2022). The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis. Diagnostics, 13(1), 45. https://doi.org/10.3390/diagnostics13010045 [Google Scholar] [Crossref]

3. Fortin, J., Leblanc, M., Elgbeili, G., Cordova, M. J., Marin, M.-F., & Brunet, A. (2021). The mental health impacts of receiving a breast cancer diagnosis: A meta-analysis. British Journal of Cancer, 125(11), 1582–1592. https://doi.org/10.1038/s41416-021-01542-3 [Google Scholar] [Crossref]

4. Siegel, R., Naishadham, D., & Jemal, A. (2013). Cancer statistics, 2013. CA: A Cancer Journal for Clinicians, 63(1), 11–30. https://doi.org/10.3322/caac.21166 [Google Scholar] [Crossref]

5. Collaborative Group on Hormonal Factors in Breast Cancer. (2002). Breast cancer and breastfeeding: Collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet (London, England), 360(9328), 187–195. https://doi.org/10.1016/S0140-6736(02)09454-0 [Google Scholar] [Crossref]

6. Chiao, J.-Y., Chen, K.-Y., Liao, K. Y.-K., Hsieh, P.-H., Zhang, G., & Huang, T.-C. (2019). Detection and classification the breast tumors using mask R-CNN on sonograms. Medicine, 98(19), e15200. https://doi.org/10.1097/MD.0000000000015200 [Google Scholar] [Crossref]

7. Zhou, L.-Q., Wang, J.-Y., Yu, S.-Y., Wu, G.-G., Wei, Q., Deng, Y.-B., Wu, X.-L., Cui, X.-W., & Dietrich, C. F. (2019). Artificial intelligence in medical imaging of the liver. World Journal of Gastroenterology, 25(6), 672–682. https://doi.org/10.3748/wjg.v25.i6.672 [Google Scholar] [Crossref]

8. Korn, R. L., Rahmanuddin, S., & Borazanci, E. (2019). Use of Precision Imaging in the Evaluation of Pancreas Cancer. Cancer Treatment and Research, 178, 209–236. https://doi.org/10.1007/978-3-030-16391-4_8 [Google Scholar] [Crossref]

9. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews. Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5 [Google Scholar] [Crossref]

10. Xu, X., Fu, L., Chen, Y., Larsson, R., Zhang, D., Suo, S., Hua, J., & Zhao, J. (2018). Breast Region Segmentation being Convolutional Neural Network in Dynamic Contrast Enhanced MRI. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018, 750–753. https://doi.org/10.1109/EMBC.2018.8512422 [Google Scholar] [Crossref]

11. Villanueva-Meyer, J. E., Chang, P., Lupo, J. M., Hess, C. P., Flanders, A. E., & Kohli, M. (2019). Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment. AJR. American Journal of Roentgenology, 212(1), 52–56. https://doi.org/10.2214/AJR.18.20328 [Google Scholar] [Crossref]

12. Truhn, D., Schrading, S., Haarburger, C., Schneider, H., Merhof, D., & Kuhl, C. (2019). Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Radiology, 290(2), 290–297. https://doi.org/10.1148/radiol.2018181352 [Google Scholar] [Crossref]

13. Herent,P. Schmauch, B. Jehanno, P. Dehaene, O. Saillard, C. Balleyguier, C. Arfi-Rouche, J. Jégou, S. (2019). Detection and characterization of MRI breast lesions using deep learning, Diagnostic and Interventional Imaging. Volume 100, Issue 4,Pages 219-225, https://doi.org/10.1016/j.diii.2019.02.008. [Google Scholar] [Crossref]

14. Antropova, N., Abe, H., & Giger, M. L. (2018). Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. Journal of medical imaging (Bellingham, Wash.), 5(1), 014503. https://doi.org/10.1117/1.JMI.5.1.014503 [Google Scholar] [Crossref]

15. Gallego-Ortiz, C., & Martel, A. L. (2019). A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions. Medical Image Analysis, 51, 116–124. https://doi.org/10.1016/j.media.2018.10.011 [Google Scholar] [Crossref]

16. Lei, Y. M., Yin, M., Yu, M. H., Yu, J., Zeng, S. E., Lv, W. Z., Li, J., Ye, H. R., Cui, X. W., & Dietrich, C. F. (2021). Artificial Intelligence in Medical Imaging of the Breast. Frontiers in oncology, 11, 600557. https://doi.org/10.3389/fonc.2021.600557 [Google Scholar] [Crossref]

17. Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA: A Cancer Journal for Clinicians, 72(1), 7–33. https://doi.org/10.3322/caac.21708 [Google Scholar] [Crossref]

18. Prevedello, L. M., Halabi, S. S., Shih, G., Wu, C. C., Kohli, M. D., Chokshi, F. H., Erickson, B. J., Kalpathy-Cramer, J., Andriole, K. P., & Flanders, A. E. (2019). Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions. Radiology. Artificial Intelligence, 1(1), e180031. https://doi.org/10.1148/ryai.2019180031 [Google Scholar] [Crossref]

19. Marinovich, M. L., Wylie, E., Lotter, W., Pearce, A., Carter, S. M., Lund, H., Waddell, A., Kim, J. G., Pereira, G. F., Lee, C. I., Zackrisson, S., Brennan, M., & Houssami, N. (2022). Artificial intelligence (AI) to enhance breast cancer screening: Protocol for population-based cohort study of cancer detection. BMJ Open, 12(1), e054005. https://doi.org/10.1136/bmjopen-2021-054005 [Google Scholar] [Crossref]

20. Sun, P., Feng, Y., Chen, C., Dekker, A., Qian, L., Wang, Z., & Guo, J. (2022). An AI model of sonographer’s evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses. Frontiers in Oncology, 12, 1022441. https://doi.org/10.3389/fonc.2022.1022441 [Google Scholar] [Crossref]

21. Sharma, S., & Mehra, R. (2020). Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—A Comparative Insight. Journal of Digital Imaging, 33(3), 632–654. https://doi.org/10.1007/s10278-019-00307-y [Google Scholar] [Crossref]

22. Burt, J. R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., Hussein, S., & Bagci, U. (2018). Deep learning beyond cats and dogs: Recent advances in diagnosing breast cancer with deep neural networks. The British Journal of Radiology, 91(1089), 20170545. https://doi.org/10.1259/bjr.20170545 [Google Scholar] [Crossref]

23. Liu, K.-L., Wu, T., Chen, P.-T., Tsai, Y. M., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W. (2020). Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: A retrospective study with cross-racial external validation. The Lancet. Digital Health, 2(6), e303–e313. https://doi.org/10.1016/S2589-7500(20)30078-9 [Google Scholar] [Crossref]

24. Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2019). VoxelMorph: A Learning Framework for Deformable Medical Image Registration. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2019.2897538 [Google Scholar] [Crossref]

25. Witowski, J., Heacock, L., Reig, B., Kang, S. K., Lewin, A., Pyrasenko, K., Patel, S., Samreen, N., Rudnicki, W., Łuczyńska, E., Popiela, T., Moy, L., & Geras, K. J. (2022). Improving breast cancer diagnostics with artificial intelligence for MRI. Science Translational Medicine, 14(664), eabo4802. https://doi.org/10.1126/scitranslmed.abo4802 [Google Scholar] [Crossref]

26. Lee, J., Kang, B. J., Kim, S. H., & Park, G. E. (2022). Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks. Diagnostics (Basel, Switzerland), 12(3), 583. https://doi.org/10.3390/diagnostics12030583 [Google Scholar] [Crossref]

27. Shah, S. M., Khan, R. A., Arif, S., & Sajid, U. (2022). Artificial intelligence for breast cancer analysis: Trends & directions. Computers in Biology and Medicine, 142, 105221. https://doi.org/10.1016/j.compbiomed.2022.105221 [Google Scholar] [Crossref]

28. Zhang, Y.-N., Xia, K.-R., Li, C.-Y., Wei, B.-L., & Zhang, B. (2021). Review of Breast Cancer Pathologigcal Image Processing. BioMed Research International, 2021, 1994764. https://doi.org/10.1155/2021/1994764 [Google Scholar] [Crossref]

29. Xue, P., Wang, J., Qin, D., Yan, H., Qu, Y., Seery, S., Jiang, Y., & Qiao, Y. (2022). Deep learning in image-based breast and cervical cancer detection: A systematic review and meta-analysis. NPJ Digital Medicine, 5, 19. https://doi.org/10.1038/s41746-022-00559-z [Google Scholar] [Crossref]

30. Freeman, K., Geppert, J., Stinton, C., Todkill, D., Johnson, S., Clarke, A., & Taylor-Phillips, S. (2021). Use of artificial intelligence for image analysis in breast cancer screening programmes: Systematic review of test accuracy. The BMJ, 374, n1872. https://doi.org/10.1136/bmj.n1872 [Google Scholar] [Crossref]

31. Mendes, J., Domingues, J., Aidos, H., Garcia, N., & Matela, N. (2022). AI in Breast Cancer Imaging: A Survey of Different Applications. Journal of Imaging, 8(9), 228. https://doi.org/10.3390/jimaging8090228 [Google Scholar] [Crossref]

32. Shao, D., Dai, Y., Li, N., Cao, X., Zhao, W., Cheng, L., Rong, Z., Huang, L., Wang, Y., & Zhao, J. (2021). Artificial intelligence in clinical research of cancers. Briefings in Bioinformatics, 23(1), bbab523. https://doi.org/10.1093/bib/bbab523 [Google Scholar] [Crossref]

33. Hickman, S. E., Woitek, R., Le, E. P. V., Im, Y. R., Mouritsen Luxhøj, C., Aviles-Rivero, A. I., Baxter, G. C., MacKay, J. W., & Gilbert, F. J. (2021). Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis. Radiology, 302(1), 88–104. https://doi.org/10.1148/radiol.2021210391 [Google Scholar] [Crossref]

34. Wei, M., Du, Y., Wu, X., Su, Q., Zhu, J., Zheng, L., Lv, G., & Zhuang, J. (2020). A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images. Computational and Mathematical Methods in Medicine, 2020, 5894010. https://doi.org/10.1155/2020/5894010 [Google Scholar] [Crossref]

35. Dutta, K., Roy, S., Whitehead, T. D., Luo, J., Jha, A. K., Li, S., Quirk, J. D., & Shoghi, K. I. (2021). Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers, 13(15), 3795. https://doi.org/10.3390/cancers13153795 [Google Scholar] [Crossref]

36. Yuan, J., Hu, Z., Mahal, B. A., Zhao, S. D., Kensler, K. H., Pi, J., Hu, X., Zhang, Y., Wang, Y., Jiang, J., Li, C., Zhong, X., Montone, K. T., Guan, G., Tanyi, J. L., Yi, F., Xu, X., Morgan, M. A., Long, M., … Zhang, L. (2018). Integrated Analysis of Genetic Ancestry and Genomic Alterations across Cancers. Cancer Cell, 34(4), 549-560.e9. https://doi.org/10.1016/j.ccell.2018.08.019 [Google Scholar] [Crossref]

37. Tagliafico, A. S., Piana, M., Schenone, D., Lai, R., Massone, A. M., & Houssami, N. (2019). Overview of radiomics in breast cancer diagnosis and prognostication. The Breast : Official Journal of the European Society of Mastology, 49, 74–80. https://doi.org/10.1016/j.breast.2019.10.018 [Google Scholar] [Crossref]

38. Talo, M. (2019). Automated classification of histopathology images using transfer learning. Artificial Intelligence in Medicine, 101, 101743. https://doi.org/10.1016/j.artmed.2019.101743 [Google Scholar] [Crossref]

39. George, K., Faziludeen, S., Sankaran, P., & Joseph K, P. (2020). Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion. Computers in Biology and Medicine, 124, 103954. https://doi.org/10.1016/j.compbiomed.2020.103954 [Google Scholar] [Crossref]

40. Monteiro, E., Costa, C., & Oliveira, J. L. (2017). A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format. Journal of Medical Systems, 41(5), 89. https://doi.org/10.1007/s10916-017-0736-1 [Google Scholar] [Crossref]

41. Lim, H. K., Hong, S. C., Jung, W. S., Ahn, K. J., Won, W. Y., Hahn, C., Kim, I. S., & Lee, C. U. (2013). Automated Segmentation of Hippocampal Subfields in Drug-Naïve Patients with Alzheimer Disease. AJNR: American Journal of Neuroradiology, 34(4), 747–751. https://doi.org/10.3174/ajnr.A3293 [Google Scholar] [Crossref]

42. Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., Patel, B. N., Yeom, K. W., Shpanskaya, K., Blankenberg, F. G., Seekins, J., Amrhein, T. J., Mong, D. A., Halabi, S. S., Zucker, E. J., … Lungren, M. P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686 [Google Scholar] [Crossref]

43. Halabi, S. S., Prevedello, L. M., Kalpathy-Cramer, J., Mamonov, A. B., Bilbily, A., Cicero, M., Pan, I., Pereira, L. A., Sousa, R. T., Abdala, N., Kitamura, F. C., Thodberg, H. H., Chen, L., Shih, G., Andriole, K., Kohli, M. D., Erickson, B. J., & Flanders, A. E. (2019). The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology, 290(2), 498–503. https://doi.org/10.1148/radiol.2018180736 [Google Scholar] [Crossref]

44. Winklhofer, S., Held, U., Burgstaller, J. M., Finkenstaedt, T., Bolog, N., Ulrich, N., Steurer, J., Andreisek, G., & Del Grande, F. (2017). Degenerative lumbar spinal canal stenosis: Intra- and inter-reader agreement for magnetic resonance imaging parameters. European Spine Journal: Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 26(2), 353–361. https://doi.org/10.1007/s00586-016-4667-1 [Google Scholar] [Crossref]

45. Mostafiz, A.M.d., Al, M., Mohammad, S.U. (2022). A machine learning approach for skin disease detection and classification using image segmentation, Healthcare Analytics, Volume 2,https://doi.org/10.1016/j.health.2022.100122. [Google Scholar] [Crossref]

46. Mohammad, R. D.,, Mahsa. D., Sara, D., Igor, B., Mohammad, H., Hamid, R.K. K., (2024). Artificial intelligence breakthroughs in pioneering early diagnosis and precision treatment of breast cancer: A multimethod study, European Journal of Cancer, Volume 209, https://doi.org/10.1016/j.ejca.2024.114227. [Google Scholar] [Crossref]

47. Yu, X., Zhao, H., Wang, R. (2024). Cancer epigenetics: from laboratory studies and clinical trials to precision medicine. Cell Death Discovery. 10, 28. https://doi.org/10.1038/s41420-024-01803-z [Google Scholar] [Crossref]

48. Sebastian, A. M., & Peter, D. (2022). Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. Life (Basel, Switzerland), 12(12), 1991. https://doi.org/10.3390/life12121991. [Google Scholar] [Crossref]

49. Iqbal, M.J., Javed, Z., Sadia, H. (2021). Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell International. 21, 270. https://doi.org/10.1186/s12935-021-01981-1 [Google Scholar] [Crossref]

50. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1-6). IEEE. https://doi.org/10.1109/CISDA.2009.5356528 [Google Scholar] [Crossref]

51. Uzun Ozsahin, D., Ikechukwu Emegano, D., Uzun, B., & Ozsahin, I. (2022). The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis. Diagnostics (Basel, Switzerland), 13(1), 45. https://doi.org/10.3390/diagnostics13010045 [Google Scholar] [Crossref]

52. Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, L., Zhao, T., Dai, J., Yang, J., Lan, L., Luo, M., Liu, Z., An, T., Zhang, B., He, X., Cong, S., Liu, X., Zhang, W., Lewis, J. P., Tiedje, J. M., Wang, Q., An, Z., Wang, F., Zhang, L., Huang, T., Lu, C., Cai, Z., Wang, F., & Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021. [Google Scholar] [Crossref]

53. Uzun O. D., Ikechukwu E.D., Uzun, B., & Ozsahin, I. (2022). The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis. Diagnostics (Basel, Switzerland), 13(1), 45. https://doi.org/10.3390/diagnostics13010045 [Google Scholar] [Crossref]

54. Khalid, A., Mehmood, A., Alabrah, A., Alkhamees, B. F., Amin, F., AlSalman, H., & Choi, G. S. (2023). Breast Cancer Detection and Prevention Using Machine Learning. Diagnostics (Basel, Switzerland), 13(19), 3113. https://doi.org/10.3390/diagnostics13193113 [Google Scholar] [Crossref]

55. Zheng, D., He, X., & Jing, J. (2023). Overview of Artificial Intelligence in Breast Cancer Medical Imaging. Journal of clinical medicine, 12(2), 419. https://doi.org/10.3390/jcm12020419 [Google Scholar] [Crossref]