Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 1, 2020

Abstract

PURPOSE: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC leads to fewer revision surgeries and consequently lower health care costs, improved cosmetic outcomes and better patient care. In this study, we propose the first use of a perioperative mass spectrometry technology (iKnife) along with a deep learning framework for detection of BCC signatures from tissue burns.

Authors

  • Alice M L Santilli
    School of Computing, Queen's University, Kingston, ON, Canada. 14amls@queensu.ca.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Natasja N Y Janssen
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Martin Kaufmann
    Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Laura Connolly
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Kaitlin Vanderbeck
    Department of Pathology, Queen's University, Kingston, ON, Canada.
  • Ami Wang
    Department of Pathology, Queen's University, Kingston, ON, Canada.
  • Doug McKay
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • John F Rudan
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Gabor Fichtinger
    Department of Mechanical and Material Engineering, Queen's University, Kingston, ON, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.

Keywords

External Resources

Popular Topics
Recent Journals