The Role of Imaging Modalities in Determining Surgical Management of Breast Cancer: A Literature Review
Abstract
Breast cancer is one of the most common malignancies among women and remains a major cause of morbidity and mortality worldwide. Because breast cancer may develop without obvious symptoms in its early stages, accurate imaging assessment is essential to support early detection, disease staging, and appropriate treatment planning. Imaging modalities such as mammography, ultrasonography, magnetic resonance imaging (MRI), dynamic contrast-enhanced MRI (DCE-MRI), and artificial intelligence-based imaging technologies play an important role in evaluating tumor size, lesion characteristics, disease extent, multifocality, and treatment response. This study aimed to examine the role of imaging modalities in determining surgical management for breast cancer. This study employed a narrative literature review with PRISMA-guided article selection. Literature was collected through searches in PubMed, Google Scholar, Garuda, and other relevant scientific databases using selected keywords, including breast cancer, imaging modalities, mammography, ultrasonography, magnetic resonance imaging, MRI, DCE-MRI, surgical management, and surgical planning. Based on the screening process, 11 articles consisting of 4 national and 7 international journals were included in the review. The findings indicate that mammography is important for early detection, ultrasonography supports lesion characterization and clinical decision-making, while MRI and DCE-MRI provide more detailed information regarding tumor extent, local staging, multifocal lesions, and response to neoadjuvant therapy. Artificial intelligence-based imaging also shows potential in improving diagnostic accuracy and supporting individualized surgical planning. Therefore, imaging modalities are not only diagnostic tools but also strategic components in determining the type and extent of surgical treatment in breast cancer patients.
References
Akbari, M., Nasiri, H., & Ziaee, M. (2024). Male breast cancer biology, screening, treatment, and follow-up: a narrative review. Iranian Journal of Public Health, 53(12), 2694. https://doi.org/10.18502/ijph.v53i12.17312
Ali, A., Alghamdi, M., Marzuki, S. S., Tengku Din, T. A. D. A. A., Yamin, M. S., Alrashidi, M., ... & Ahmed, N. (2025). Exploring AI approaches for breast cancer detection and diagnosis: A review Article. Breast Cancer: Targets and Therapy, 927-947. https://doi.org/10.2147/bctt.s550307
Arslan, M., Asim, M., Sattar, H., Khan, A., Ali, F. T., Zehra, M., & Talluri, K. (2024). Role of radiology in the diagnosis and treatment of breast cancer in women: a comprehensive review. Cureus, 16(9). https://doi.org/10.7759/cureus.70097
Azahra, S., Kusumaningtyas, P., & Rofi’i, M. (2025). Deteksi Dini Kanker Payudara Menggunakan Citra Ultrasonografi Berbasis Convolutional Neural Networks dan Particle Swarm Optimization. Journal of Computer System and Informatics (JoSYC). https://doi.org/10.47065/josyc.v6i2.6637
Bagheriye, L., & Kwisthout, J. (2025). Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques. Frontiers in Oncology, 15, 1468753. https://doi.org/10.3389/fonc.2025.1468753
Bisoyi, P. (2022). A brief tour guide to cancer disease. In Understanding cancer (pp. 1-20). Academic Press. https://doi.org/10.1016/B978-0-323-99883-3.00006-8
Boire, A., Burke, K., Cox, T. R., Guise, T., Jamal-Hanjani, M., Janowitz, T., ... & Sahai, E. (2024). Why do patients with cancer die?. Nature Reviews Cancer, 24(8), 578-589. https://doi.org/10.1038/s41568-024-00708-4
Brown, J. S., Amend, S. R., Austin, R. H., Gatenby, R. A., Hammarlund, E. U., & Pienta, K. J. (2023). Updating the definition of cancer. Molecular Cancer Research, 21(11), 1142-1147. https://doi.org/10.1158/1541-7786.MCR-23-0411
Cao, W., Qin, K., Li, F., & Chen, W. (2024). Comparative study of cancer profiles between 2020 and 2022 using global cancer statistics (GLOBOCAN). Journal of the National Cancer Center, 4(2), 128-134. https://doi.org/10.1016/j.jncc.2024.05.001
Chaddad, A., Tan, G., Liang, X., Hassan, L., Rathore, S., Desrosiers, C., ... & Niazi, T. (2023). Advancements in MRI-based radiomics and artificial intelligence for prostate cancer: a comprehensive review and future prospects. Cancers, 15(15), 3839. https://doi.org/10.3390/cancers15153839
Conte, L., De Nunzio, G., Lupo, R., Mieli, M., Lezzi, A., Vitale, E., ... & Federico, M. (2023). Breast cancer prevention: the key role of population screening, breast self-examination (BSE) and technological tools. Survey of Italian women. Journal of Cancer Education, 38(5), 1728-1742. https://doi.org/10.1007/s13187-023-02327-3
Dong, Z., Chen, M., Zhou, Y., Pang, M., Zhang, D., Wang, X., ... & Zheng, A. (2025). Dynamic MRI Assessment of Early Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Real-World Study. International Journal of Surgery. https://doi.org/10.1186/s13058-025-02009-6
Huang, J., Xiang, Y., Gan, S., Wu, L., Yan, J., Ye, D., & Zhang, J. (2025). Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach. Discover Oncology, 16(1), 1625. https://doi.org/10.1007/s12672-025-03307-3
Hussain, D., Al-Masni, M. A., Aslam, M., Sadeghi-Niaraki, A., Hussain, J., Gu, Y. H., & Naqvi, R. A. (2024). Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations. Journal of X-Ray Science and Technology, 32(4), 857-911. https://doi.org/10.3233/xst-230429
Jahan, S. (2026). Understanding Rural Women’s Intention toward Breast Cancer Screening: A qualitative study using the Theory of Planned Behavior in Bangladesh (Doctoral dissertation).
Jain, B. P., & Pandey, S. (Eds.). (2022). Understanding cancer: from basics to therapeutics. Academic Press.
Kang, M. J., Jung, K. W., Bang, S. H., Choi, S. H., Park, E. H., Yun, E. H., ... & Seo, H. G. (2023). Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2020. Cancer Research and Treatment: Official Journal of Korean Cancer Association, 55(2), 385-399. https://doi.org/10.4143/crt.2023.447
Kim, J., Harper, A., McCormack, V., Sung, H., Houssami, N., Morgan, E., ... & Fidler-Benaoudia, M. M. (2025). Global patterns and trends in breast cancer incidence and mortality across 185 countries. Nature medicine, 31(4), 1154-1162. https://doi.org/10.1038/s41591-025-03502-3
Kumar, M. A., Baba, S. K., Khan, I. R., Khan, M. S., Husain, F. M., Ahmad, S., ... & Bhat, A. A. (2025). Glutamine metabolism: molecular regulation, biological functions, and diseases. MedComm, 6(7), e70120. https://doi.org/10.1002/mco2.70120
Lemij, A. A., Bastiaannet, E., De Glas, N. A., van den Bos, F., Portielje, J. E., Liefers, G. J., & Derks, M. G. (2023). Breast cancer in the older population: a global challenge—an epidemiological perspective. Annals of Breast Surgery, 7.
Li, X., Fu, P., Jiang, M., Zhang, J., Tan, L., Ai, T., & Li, X. (2021). The diagnostic performance of dynamic contrast-enhanced MRI and its correlation with subtypes of breast cancer. Medicine, 100(51), e28109. https://doi.org/10.1097/MD.0000000000028109
Liu, H. (2024). Traditional Chinese medicine for visceral pain. In Visceral pain: from bench to bedside (pp. 119-166). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-9167-9_8
Liu, Y., Liao, X., He, Y., He, F., Ren, J., Zhou, P., & Zhang, X. (2025). Tumor size and stage assessment accuracy of MRI and ultrasound versus pathological measurements in early breast cancer patients. BMC Women's Health, 25(1), 159. https://doi.org/10.1186/s12905-025-03679-2
Marinovich, M. L., Houssami, N., Spillane, A., Mann, G. B., Taylor, D., Reintals, M., ... & Saunders, C. M. (2025). Changes in patient management after preoperative MRI for newly diagnosed breast cancer: a multicentre prospective observational study. Medical Journal of Australia, 223(11), 602-610. https://doi.org/10.5694/mja2.70051
Mayer-Zugai, K., Georgiadou, I., Weiss, C., Ast, A., & Scheffel, H. (2025). The Impact of Preoperative Breast Magnetic Resonance Imaging on Surgical Planning: A Retrospective Single-Center Study. Anatomia, 4(3), 11. https://doi.org/10.3390/anatomia4030011
Merjane, V., Perin, D. M. P., Bacha, P. M. G. E., Miranda, B. M. M., Bitencourt, A. G. V., & Iared, W. (2024). Breast Imaging Reporting and Data System (BI-RADS®): a success history and particularities of its use in Brazil. Revista Brasileira de Ginecologia e Obstetrícia, 46, e-rbgo6. https://doi.org/10.61622/rbgo/2024ar06
Naz, S., Thanasilp, S., & Wisesrith, W. (2024). Empowering Health: A Comprehensive Concept Analysis of Breast Self-Examination for Proactive Breast Health Management. National Journal of Community Medicine, 15(03), 238-243. https://doi.org/10.55489/njcm.150320243682
Nowak, H., Szwacka, D. M., Pater, M., Mrugalski, W. K., Milczarek, M. G., Staniszewska, M., ... & Barciszewska, A. M. (2022). Holistic approach to the diagnosis and treatment of patients with tumor metastases to the spine. Cancers, 14(14), 3480. https://doi.org/10.3390/cancers14143480
Oglat, A. A., AbuKhalil, T., Hasan, H., Isawi, I. H., Oqlat, A. A., Abu Mhanna, H. Y., & Akhdar, H. F. (2024). Evaluating the use of breast self-examination (BSE) for recognizing breast Cancer awareness among Jordanian students and workers in Medical Fields. International Journal of Women's Health, 1743-1753. https://doi.org/10.2147/IJWH.S485116
Ozsahin, I., Uzun, B., Mustapha, M. T., Usanese, N., Yuvali, M., & Ozsahin, D. U. (2024). BI-RADS-based classification of breast cancer mammogram dataset using six stand-alone machine learning algorithms. In Artificial intelligence and image processing in medical imaging (pp. 195-216). Academic Press. https://doi.org/10.1016/B978-0-323-95462-4.00008-X
Singh, A., & Roghini, S. (2023). Cancer: unraveling the complexities of uncontrolled growth and metastasis. PEXACY International Journal of Pharmaceutical Science, 2(8), 59-73. https://doi.org/10.5281/zenodo.8266559
Soekersi, H., Azhar, Y., & Akbari, K. S. (2022). Peran mammografi untuk skrining kanker payudara: Sebuah tinjauan pustaka. Journal Of The Indonesian Medical Association, 72(3), 144-150. https://doi.org/10.47830/jinma-vol.72.3-2022-627
Song, X., Xu, H., Wang, X., Liu, W., Leng, X., Hu, Y., ... & Ma, B. (2024). Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer. BMC Women's Health, 24(1), 380. https://doi.org/10.1186/s12905-024-03231-8
Wang, M., Mo, S., Li, G., Zheng, J., Wu, H., Tian, H., ... & Dong, F. (2025). Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes. Breast Cancer Research, 27(1), 161. https://doi.org/10.1186/s13058-025-02113-7
Wang, W. L., Hao, Y. H., Pang, X., & Tang, Y. L. (2025). Cancer pain: molecular mechanisms and management. Molecular Biomedicine, 6(1), 45. https://doi.org/10.1186/s43556-025-00289-0
Wu, J., Li, Y., Gong, W., Li, Q., Han, X., & Zhang, T. (2025). Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography. BMC Medical Imaging, 25(1), 265. https://doi.org/10.1186/s12880-025-01767-1
Zafar, T., Naik, A. Q., Kumar, M., & Shrivastava, V. K. (2022). Epidemiology and risk factors of breast cancer. In Breast cancer: From bench to personalized medicine (pp. 3-29). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0197-3_1
Copyright (c) 2026 Journal La Medihealtico

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



