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Abstract #4127

Towards Deep Learning-Driven Assessment of Lesion Biopsy in Breast MRI

Louisa Fay1,2,3, Stephanie Schmidt2, Bin Yang2, Moritz T. Winkelmann4, Thomas Kuestner1, and Felix Peisen4
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3Stanford Medicine, Department of Radiology, Stanford, CA, United States, 4Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany

Synopsis

Keywords: Analysis/Processing, Breast, MR-Guided Breast Cancer Biopsy, Deep Learning, Real-time tracking

Motivation: MR-guided biopsies are critical for breast cancer diagnosis, but accurate representation of lesions is challenging, often requiring follow-up imaging that increases patient burden and delays diagnosis.

Goal(s): This study aims to develop a real-time tracking system by identifying lesions, position-marker, needle-tips during breast MR-guided biopsies.

Approach: We employed a Template-Matching-Network, trained on 235 patients’ breast-MRI scans to predict in real-time lesion location, position-marker, and needle-tip during MR-guided breast biopsy.

Results: Our model demonstrated high localization accuracy within images, but requires further adjustment cross-images. Results indicate potential to streamline biopsies and minimize follow-up imaging. Future research will focus on assessing adequacy for breast-MRI biopsy.

Impact: This study introduces a real-time MR-guided breast biopsies tracking system, enhancing localization accuracy of lesions, position-markers, and needle-tips. This approach aims to improve patient outcomes, optimize breast cancer diagnostic confidence, and reduce the need for follow-up examinations.

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