Streeter Samuel S, Hunt Brady, Paulsen Keith D, Pogue Brian W
Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, USA.
Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, 1 Medical Center Drive, Lebanon, NH 03756, USA.
Curr Opin Biomed Eng. 2022 Jun;22. doi: 10.1016/j.cobme.2022.100382. Epub 2022 Mar 28.
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.
保乳手术要求切除边缘无癌，但在过去十年的大部分时间里，由于切缘阳性导致的再次切除率一直接近20%，各手术中心之间差异很大。最近的研究表明，鉴于图像重建速度和对所有边缘的全三维感知，容积式X射线成像在边缘评估方面优于标准技术。用于容积式医学图像数据自动分析的深度学习方法越来越受到关注，并且在简化手术中样本成像的临床工作流程方面可能发挥重要作用。目前临床研究中使用的X射线成像系统存在肿瘤与纤维腺组织对比度差的问题，这促使开发辅助工具，这些工具可能会补充容积式X射线扫描，并通过为外科医生提供实时增强引导，进一步改善手术中边缘评估的未来。
