| Literature DB >> 35600140 |
Samuel S Streeter1, Brady Hunt1, Keith D Paulsen1,2, Brian W Pogue1,2.
Abstract
Breast-conserving surgery requires that resection margins be cancer-free, but re-excision rates due to positive margins have remained near 20% for much of the last decade with high variability between surgical centers. Recent studies have demonstrated that volumetric X-ray imaging improves margin assessment over standard techniques, given the speed of image reconstruction and full three-dimensional sensing of all margins. Deep learning approaches for automated analysis of volumetric medical image data are gaining traction and could play an important role streamlining the clinical workflow for intra-surgical specimen imaging. X-ray imaging systems currently deployed in clinical studies suffer from poor tumor-to-fibroglandular tissue contrast, motivating the development of adjuvant tools that could potentially complement volumetric X-ray scanning and further improve the future of intra-surgical margin assessment by real-time augmented guidance for the surgeon.Entities:
Keywords: Breast-conserving surgery; Deep learning; Margin assessment; Micro-computed tomography; Tomosynthesis
Year: 2022 PMID: 35600140 PMCID: PMC9119412 DOI: 10.1016/j.cobme.2022.100382
Source DB: PubMed Journal: Curr Opin Biomed Eng ISSN: 2468-4511