Emerging and future use of intra-surgical volumetric X-ray imaging and adjuvant tools for decision support in breast-conserving surgery. (June 2022)

• Record Type: Journal Article
• Authors: Streeter, Samuel S.; Hunt, Brady; Paulsen, Keith D.; Pogue, Brian W.
• Is Part Of: Current opinion in biomedical engineering. Volume 22(2022)
• Publication Date: 2022-06
• Subjects: Breast-conserving surgery -- Margin assessment -- Micro-computed tomography -- Tomosynthesis -- Deep learning; Biomedical engineering -- Periodicals; 610.28
• DOI: 10.1016/j.cobme.2022.100382
• Languages: English
• ISSNs: 2468-4511

Abstract

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. Graphical abstract: Image 1 Highlights: Volumetric X-ray imaging shows strong potential for improving breast-conserving surgery (BCS) margin assessment. X-ray imaging lacks contrast between fibrous breast tissues, suggesting value in multimodal solutions using adjuvant tools. Advances in computational decision support and clinical interaction with and interpretation of volumetric data are needed. Collaboration between clinicians and engineers is needed to advance these emerging tools toward translation in BCS.