AI IN ONCOLOGY - DIFFERENTIATING BETWEEN BENIGN MASSES AND MALIGNANT TUMORS IN THE CASE OF BREAST CANCER
Abstract
Introduction: Cancer poses a considerable challenge to worldwide healthcare systems. Presently, researchers globally are striving to enhance diagnostics and early diagnosis of the disease, which may decrease mortality rates among cancer patients and influence life expectancy. The application of artificial intelligence in medicine, especially in cancer diagnosis, signifies a substantial development. Radiomics plays a crucial role in this field, as it is utilized to augment the effectiveness of image processing for precise early breast cancer diagnosis and to boost overall treatment outcomes.
Aim of the study: The main aim of this work is to clarify the essential ideas related to artificial intelligence, such as machine learning, deep learning, and radiomics, in a comprehensible way. Another aspect was to illustrate the great potential of employing this technology in cancer diagnostics, especially for breast cancer.
Materials and methods: A review of the literature available in the PubMed database was performed, using the key words: „artificial intelligence", „machine learning" ; „deep learning”, „radiomics”, „breast cancer”, „cancer”, „oncology”.
Conclusion: Artificial intelligence is prevalent in various domains, particularly in medicine, with a focus on oncological diagnosis. Its application can facilitate early disease detection and prompt treatment. Artificial intelligence possesses several limits, presenting a significant challenge for researchers in this domain. Further research on the advancement and enhancement of artificial intelligence methodologies is essential.
References
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. In npj Digital Medicine (Vol. 4, Issue 1). Nature Research. https://doi.org/10.1038/s41746-021-00438-z
Beuque, M. P. L., Lobbes, M. B. I., van Wijk, Y., Widaatalla, Y., Primakov, S., Majer, M., Balleyguier, C., Woodruff, H. C., & Lambin, P. (2023). Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms. Radiology, 307(5). https://doi.org/10.1148/radiol.221843
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021). Artifi Cial intelligence in cancer research and precision medicine. In Cancer Discovery (Vol. 11, Issue 4, pp. 900–915). American Association for Cancer Research Inc. https://doi.org/10.1158/2159-8290.CD-21-0090
Bickelhaupt, S., Jaeger, P. F., Laun, F. B., Lederer, W., Daniel, H., Kuder, T. A., Wuesthof, L., Paech, D., Bonekamp, D., Radbruch, A., Delorme, S., Schlemmer, H.-P., Steudle, F. H., & Maier-Hein, K. H. (2018). Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer. Radiology, 287(3), 761–770. https://doi.org/10.1148/radiol.2017170273
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229–263. https://doi.org/10.3322/caac.21834
Desautels, J. E. L., Rangayyan, R., & Mudigonda, N. R. (2000). Gradient and texture analysis for the classification of mammographic masses. IEEE Transactions on Medical Imaging, 19(10), 1032–1043. https://doi.org/10.1109/42.887618
Drukker, K., Giger, M. L., Joe, B. N., Kerlikowske, K., Greenwood, H., Drukteinis, J. S., Niell, B., Fan, B., Malkov, S., Avila, J., Kazemi, L., & Shepherd, J. (2019). Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology, 290(3), 621–628. https://doi.org/10.1148/radiol.2018180608
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577. https://doi.org/10.1148/radiol.2015151169
Halligan, Steve et al. “Why did European Radiology reject my radiomic biomarker paper? How to correctly evaluate imaging biomarkers in a clinical setting.” In European radiology vol. 31,12 (2021): 9361-9368. doi:10.1007/s00330-021-07971-1
Iranmakani, S., Mortezazadeh, T., Sajadian, F., Ghaziani, M. F., Ghafari, A., Khezerloo, D., & Musa, A. E. (2020). A review of various modalities in breast imaging: technical aspects and clinical outcomes. In Egyptian Journal of Radiology and Nuclear Medicine (Vol. 51, Issue 1). Springer. https://doi.org/10.1186/s43055-020-00175-5
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., Van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., & Aerts, H. J. W. L. (2012). Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48(4), 441–446. https://doi.org/10.1016/j.ejca.2011.11.036
Li, H., Mendel, K. R., Lan, L., Sheth, D., & Giger, M. L. (2019). Digital mammography in breast cancer: Additive value of radiomics of breast parenchyma. Radiology, 291(1), 15–20. https://doi.org/10.1148/radiol.2019181113
Liu, Z., Wang, S., Dong, D., Wei, J., Fang, C., Zhou, X., Sun, K., Li, L., Li, B., Wang, M., & Tian, J. (2019). The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges. In Theranostics (Vol. 9, Issue 5, pp. 1303–1322). Ivyspring International Publisher. https://doi.org/10.7150/thno.30309
Majumder, A., & Sen, D. (2021). Artificial intelligence in cancer diagnostics and therapy: Current perspectives. In Indian Journal of Cancer (Vol. 58, Issue 4, pp. 481–492). Wolters Kluwer Medknow Publications. https://doi.org/10.4103/ijc.IJC_399_20
Parasher, G., Wong, M., & Rawat, M. (2020). Evolving role of artificial intelligence in gastrointestinal endoscopy. In World Journal of Gastroenterology (Vol. 26, Issue 46, pp. 7287–7298). Baishideng Publishing Group Co. https://doi.org/10.3748/wjg.v26.i46.7287
Qi, Y. J., Su, G. H., You, C., Zhang, X., Xiao, Y., Jiang, Y. Z., & Shao, Z. M. (2024). Radiomics in breast cancer: Current advances and future directions. In Cell reports. Medicine (Vol. 5, Issue 9, p. 101719). https://doi.org/10.1016/j.xcrm.2024.101719
Stanicki, P., Nowakowska, K., Piwoński, M., Żak, K., Niedobylski, S., Zaremba, B., & Oszczędłowski, P. (2021). The role of artificial intelligence in cancer diagnostics - a review. Journal of Education, Health and Sport, 11(9), 113–122. https://doi.org/10.12775/jehs.2021.11.09.016
Tang X. The role of artificial intelligence in medical imaging research. In BJR Open. 2019 Nov 28;2(1):20190031. doi: 10.1259/bjro.20190031. PMID: 33178962; PMCID: PMC7594889.
Tran, W. T., Jerzak, K., Lu, F. I., Klein, J., Tabbarah, S., Lagree, A., Wu, T., Rosado-Mendez, I., Law, E., Saednia, K., & Sadeghi-Naini, A. (2019). Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. In Journal of Medical Imaging and Radiation Sciences (Vol. 50, Issue 4, pp. S32–S41). Elsevier Inc. https://doi.org/10.1016/j.jmir.2019.07.010
Wang, S., & Summers, R. M. (2012). Machine learning and radiology. In Medical Image Analysis (Vol. 16, Issue 5, pp. 933–951). https://doi.org/10.1016/j.media.2012.02.005
Zhang, J., Zhan, C., Zhang, C., Song, Y., Yan, X., Guo, Y., Ai, T., & Yang, G. (2023). Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features. La Radiologia Medica, 128(2), 160–170. https://doi.org/10.1007/s11547-023-01594-w
Views:
32
Downloads:
9
Copyright (c) 2025 Patrycja Jędrzejewska-Rzezak, Monika Czekalska, Natalia Kulicka, Kinga Knutelska, Aleksandra Winsyk, Paulina Gajniak, Maciej Karwat, Tytus Tyralik, Klaudia Bilińska, Joanna Węgrzecka
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
