Background: The aim of this study was to report on a cohort of 100 patients where the Magseed paramagnetic marker was used to localize non-palpable breast lesions.
Methods: Data were collected from a cohort of 100 patients with non-palpable breast lesions, who underwent localization using the Magseed marker. This marker consists of a paramagnetic seed that can be seen on mammography or ultrasound and intraoperatively detected with the use of the Sentimag probe. The data were collected over a period of 23 months (May 2019 to April 2021).
Results: All 111 seeds were successfully placed in the breasts of 100 patients under ultrasound or via stereotactic guidance. Eighty-nine seeds were inserted in single lesions or small microcalcification clusters in a single breast, 12 seeds were deployed to a bracket microcalcification clusters and 10 to help localize two tumors within the same breast. Most Magseed markers (88.3%) were placed in the center of the lesion (≤1 mm). The re-excision rate was 5%. All Magseed markers were successfully retrieved and no surgical complications were observed.
Conclusions: This study reports our experience in a Belgian breast unit using the Magseed magnetic marker and it highlights the many advantages of the Magseed marker system. With this system, we successfully identified subclinical breast lesions and extended microcalcification clusters, targeting multiple sites within the same breast.
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http://dx.doi.org/10.21037/gs-22-552 | DOI Listing |
BMC Cancer
January 2025
Unité de Sénologie, Centre Jean PERRIN, Clermont-Ferrand, France.
Background: Most breast cancers are detected at an early stage in which case conservative surgery is indicated. An accurate preoperative localization technique is essential for conservative surgery of non-palpable breast lesions. Currently, the gold standard technique is wire localization (WL).
View Article and Find Full Text PDFSurgery
January 2025
Breast Surgery Unit, Veneto Institute of Oncology IOV, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Padova, Italy.
Background: Intraoperative ultrasound-guided breast-conserving surgery guarantees real-time direct visualization of tumor and resection margins. We compared surgical, oncologic, and cosmetic outcomes between intraoperative ultrasound-guided breast-conserving surgery and traditional (palpation- or wire-guided) surgery across all breast cancer lesion types.
Methods: This prospective observational cohort study was conducted at the Veneto Institute of Oncology between January 2021 and October 2022.
Phys Med Biol
January 2025
Department of Medical Physics, Jeroen Bosch Ziekenhuis, Henri Dunantstraat 1, 's-Hertogenbosch, 5223GZ, NETHERLANDS.
The treatment of breast cancer during pregnancy requires careful consideration of consequences for both maternal and fetal health. In non-pregnant patients, the use of radioactive iodine-125 (125I)-seeds is standard practice for localising non-palpable breast tumors before breast-conserving surgery. However, the use of 125I-seeds in pregnant patients has been avoided due to concerns about fetal radiation exposure.
View Article and Find Full Text PDFEur J Radiol Open
June 2025
Radiology Department, National Cancer Institute, Cairo University, Egypt.
Purpose: To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.
Methods And Materials: Mammograms done in 2020-2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year's result (2019-2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications.
J Imaging Inform Med
December 2024
Tecgraf Institute and Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Mammography images are widely used to detect non-palpable breast lesions or nodules, aiding in cancer prevention and enabling timely intervention when necessary. To support medical analysis, computer-aided detection systems can automate the segmentation of landmark structures, which is helpful in locating abnormalities and evaluating image acquisition adequacy. This paper presents a deep learning-based framework for segmenting the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue in standard-view mammography images.
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