Utilization of breast magnetic resonance imaging in the pre-operative setting and its ability to better predict local staging in non-metastatic breast cancer patients
DOI:
https://doi.org/10.18203/2349-2902.isj20243981Keywords:
Breast cancer, Ductal carcinoma in situ, Magnetic resonance imaging, Staging, HistologyAbstract
Background: Breast cancer can be difficult to stage using ultrasound (USS) and mammography (MMG) alone. The use of MRI in the pre-operative setting has been increasingly incorporated to aid in treatment planning. However, the indications for breast magnetic resonance imaging (MRI) are not universally agreed upon, and its direct impact on patient care requires further research. This paper aims to assess pre-operative non-metastatic breast cancer patients to determine whether breast MRI led to changes in management and to evaluate its accuracy in predicting tissue staging when compared to ultrasound and mammography.
Method: This is a retrospective study of 44 patients diagnosed with non-metastatic breast cancer who underwent upfront surgery and received pre-operative breast MRI between March 1, 2022, and March 1, 2024. Patient demographics, pre-operative imaging (including USS, MMG, and MRI), and final tissue histology were reviewed. The findings from breast MRI and its impact on pre-operative planning, as well as its concordance with final tissue histological staging, were assessed.
Results: Of the 44 patients who underwent pre-operative breast MRI, 12 experienced a change in management, and 11 required additional investigations. This was largely due to an upstaging of the disease, leading to mastectomy instead of the previously planned breast-conserving surgery. Furthermore, breast MRI demonstrated better concordance with final histological staging at 69.4% compared to USS/MMG alone at 52.7%.
Conclusion: Breast MRI is a valuable tool in pre-operative planning for newly diagnosed breast cancer patients. It can significantly affect surgical management in a substantial proportion of patients and has been shown to be more accurate in estimating disease burden.
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References
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