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

被引:1
作者
Streeter, Samuel S. [1 ]
Hunt, Brady [1 ]
Paulsen, Keith D. [1 ,2 ]
Pogue, Brian W. [1 ,2 ]
机构
[1] Dartmouth Coll, Thayer Sch Engn, 14 Engn Dr, Hanover, NH 03755 USA
[2] Dartmouth Hitchcock Med Ctr, Norris Cotton Canc Ctr, 1 Med Ctr Dr, Lebanon, NH 03756 USA
基金
美国国家卫生研究院;
关键词
Breast-conserving surgery; Margin assessment; Micro-computed to-mography; Tomosynthesis; DIGITAL MAMMOGRAPHY; MARGIN ASSESSMENT; RE-EXCISION; CANCER; LUMPECTOMY; SPECIMEN; TOMOSYNTHESIS; SEGMENTATION;
D O I
10.1016/j.cobme.2022.100382
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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-tofibroglandular tissue contrast, motivating the development of adjuvant tools that could potentially complement volumetric Xray scanning and further improve the future of intra-surgical margin assessment by real-time augmented guidance for the surgeon.
引用
收藏
页数:10
相关论文
共 52 条
  • [1] Margin Re-excision and Local Recurrence in Invasive Breast Cancer: A Cost Analysis Using a Decision Tree Model
    Abe, Shoko E.
    Hill, Joshua S.
    Han, Yimei
    Walsh, Kendall
    Symanowski, James T.
    Hadzikadic-Gusic, Lejla
    Flippo-Morton, Teresa
    Sarantou, Terry
    Forster, Meghan
    White, Richard L., Jr.
    [J]. JOURNAL OF SURGICAL ONCOLOGY, 2015, 112 (04) : 443 - 448
  • [2] Digital breast tomosynthesis versus full-field digital mammography-Which modality provides more accurate prediction of margin status in specimen radiography?
    Amer, Heba A.
    Schmitzberger, Florian
    Ingold-Heppner, Barbara
    Kussmaul, Julia
    El Tohamy, Manal F.
    Tantawy, Hazim I.
    Hamm, B.
    Makowski, M.
    Fallenberg, Eva M.
    [J]. EUROPEAN JOURNAL OF RADIOLOGY, 2017, 93 : 258 - 264
  • [3] Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review
    Bai, Jun
    Posner, Russell
    Wang, Tianyu
    Yang, Clifford
    Nabavi, Sheida
    [J]. MEDICAL IMAGE ANALYSIS, 2021, 71
  • [4] MarginProbeA© reduces the rate of re-excision following breast conserving surgery for breast cancer
    Blohmer, Jens-Uwe
    Tanko, Julia
    Kueper, Janina
    Gross, Jessica
    Voelker, Ragna
    Machleidt, Anna
    [J]. ARCHIVES OF GYNECOLOGY AND OBSTETRICS, 2016, 294 (02) : 361 - 367
  • [5] Chen Y-C, 2019, J IMAGE GRAPHICS, V7
  • [6] The role of Micro-CT in imaging breast cancer specimens
    DiCorpo, Daniel
    Tiwari, Ankur
    Tang, Rong
    Griffin, Molly
    Aftreth, Owen
    Bautista, Pinky
    Hughes, Kevin
    Gershenfeld, Neil
    Michaelson, James
    [J]. BREAST CANCER RESEARCH AND TREATMENT, 2020, 180 (02) : 343 - 357
  • [7] Intra-operative assessment of excised breast tumour margins using ClearEdge imaging device
    Dixon, J. M.
    Renshaw, L.
    Young, O.
    Kulkarni, D.
    Saleem, T.
    Sarfaty, M.
    Sreenivasan, R.
    Kusnick, C.
    Thomas, J.
    Williams, L. J.
    [J]. EJSO, 2016, 42 (12): : 1834 - 1840
  • [8] Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus irradiation for the treatment of invasive breast cancer
    Fisher, B
    Anderson, S
    Bryant, J
    Margolese, RG
    Deutsch, M
    Fisher, ER
    Jeong, J
    Wolmark, N
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2002, 347 (16) : 1233 - 1241
  • [9] Intraoperative digital breast tomosynthesis using a dedicated device is more accurate than standard intraoperative mammography for identifying positive margins
    Garlaschi, A.
    Fregatti, P.
    Oddone, C.
    Friedman, D.
    Houssami, N.
    Calabrese, M.
    Tagliafico, A. S.
    [J]. CLINICAL RADIOLOGY, 2019, 74 (12) : 974.e1 - 974.e6
  • [10] Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer
    Gholizadeh-Ansari, Maryam
    Alirezaie, Javad
    Babyn, Paul
    [J]. JOURNAL OF DIGITAL IMAGING, 2020, 33 (02) : 504 - 515