Stereotactic-Guided Magnetic Seed Localisation Versus Radioguided Occult Lesion Localisation: A Comparison of Total Resection Volumes
Introduction: Cosmetic outcome after breast conservation surgery has a major impact on patients' quality of life.Previous research demonstrated the use of non-radioactive magnetic markers (Magseed) to be safe and effective.There have been few studies comparing magnetic seeds and radioguided occult lesion localisation (ROLL).This study compares the total resection volume in lumpectomies of mammographically detected non-palpable lesions using magnetic seeds with the volume resulting from ROLL guidance. Methods: This was a retrospective cohort study comparing lumpectomy cases guided by one or the other technique. Total resection volume was calculated based on pathology reports. Margin clearance and reoperation rates were analysed.Results: Each cohort included 11 patients with similar baseline characteristics and comparable histopathology from the vacuum-assisted biopsy specimens.The technical success rates of magnetic seed deployment and ROLL injection were both 100%.The total resection volume in the magnetic seed cohort was significantly lower than that in the ROLL cohort.If the cases with involved or close margins were excluded from analysis of total resection volume, the magnetic seeds group still achieved a significantly lower total resection volume.No significant difference was found in the final histopathological diagnosis, margin clearance, or reoperation rates between the two groups.Conclusion: Magnetic seed localisation is a safe and effective technique that can reduce total resection volume compared with ROLL, without compromising margin clearance and reoperation rates.
Authors
- YH Ling
- AYT Lai (ORCID: https://orcid.org/0000-0002-7446-3134)
- Jyw Chan
- KH Chin
- LWY Ma
- AHC Wong
- WWC Wong
- RYS Mak
- CY Choi
- JSY Lee
- CKM Mo
- P. Y. Chau
Institutions
- Pamela Youde Nethersole Eastern Hospital (CN)
- Ruttonjee Hospital (CN)
Publication Details
- Journal
- Hong Kong Journal of Radiology
- Published
- 2024-09-10
- DOI
- https://doi.org/10.12809/hkjr2317719
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00