Achieving microscopically negative margins (R0) remains one of the main challenges in breast-conserving surgery (BCS), directly influencing local control, cosmetic outcome, and the need for re-excision. Despite advances in localization techniques such as seed placement and intraoperative pathology, incomplete resections still occur in up to 20% of cases. Recent technological developments now offer the opportunity to redefine margin assessment through multimodal intraoperative molecular imaging, artificial intelligence (AI), and mixed reality (AR/VR) visualization. Preoperative imaging – spanning magnetic resonance imaging (MRI), ultrasound, and combined positron-emission tomography (PET) / computed tomography (CT) – provides functional information that guides personalized surgical planning. AI-based segmentation and radiomics enable integration of these datasets into three-dimensional models for accurate tumor mapping and prediction of disease spread. Intraoperatively, radioguided surgery (RGS), intraoperative ultrasound, and fluorescence or gamma systems offer real-time navigation, while label-free techniques such as Raman spectroscopy, impedance analysis, and hyperspectral imaging add biochemical insight at the resection margin. AR/VR tools further enhance spatial orientation by fusing preoperative and intraoperative data into interactive 3D environments. After excision, specimen imaging with micro-CT, Cerenkov luminescence, and high-resolution PET/CT provides immediate verification of margin status. Emerging AI algorithms can interpret these multimodal images in real time, supporting rapid intraoperative decision-making. Together, these innovations define a continuum of planning, navigation, and verification that progressively reduces uncertainty around surgical margins. The convergence of molecular imaging, digital visualization, and AI marks a decisive step toward consistently R0, biologically informed, and truly personalized breast-conserving surgery.
Building similarity graph...
Analyzing shared references across papers
Loading...
Thomas Wendler
Bieke Lambert
John R. Orozco Cortés
Revista de Senología y Patología Mamaria
Technical University of Munich
Ghent University Hospital
University of Augsburg
Building similarity graph...
Analyzing shared references across papers
Loading...
Wendler et al. (Thu,) studied this question.
synapsesocial.com/papers/69b64d48b42794e3e660e0f7 — DOI: https://doi.org/10.1016/j.senol.2026.100757