Radioactive Seed vs. Wire: Rethinking Breast Cancer Localization

In this episode of Surgical Readings, host Rick Greene, MD, FACS, connects with Lejla Hadzikadic-Gusic, MD, MS, FACS, for an engaging conversation about a study that is reshaping the way surgeons localize nonpalpable malignant breast disease. Dr. Hadzikadic-Gusic discusses her randomized trial, published in the Annals of Surgery (A Randomized, Single-Center, Superiority Trial of Radioactive Seed Location versus Wire Localization for Malignant Breast Disease), comparing two commonly used localization methods: traditional wire placement and radioactive seed technique. Listeners will hear how her team set out to answer an important question in breast surgery: Can we improve the patient experience without compromising clinical outcomes? While both techniques performed similarly in achieving clear surgical margins, the study uncovered compelling differences in what matters to patients and care teams. Tune in to learn how radioactive seed localization led to higher satisfaction, less anxiety, and greater convenience for patients—and offered workflow advantages for surgeons. Talk about the podcast on social media using the hashtag #SurgicalReadings   Copyright © 2026 by the American College of Surgeons (ACS). All rights reserved.   The contents of these materials may be cited in academic publications but otherwise may not be reproduced, disseminated, or transmitted in any form by any means without the express written permission of ACS. These materials may not be resold nor used to create revenue-generating content by any entity other than the ACS without the express written permission of the ACS. The contents of these materials are strictly prohibited from being uploaded, shared, or incorporated in any third-party applications, platforms, software, or websites without prior written authorization from the ACS. This restriction explicitly includes, but is not limited to, the integration of ACS content into tools leveraging artificial intelligence (AI), machine learning, large language models, or generative AI technologies and infrastructures.