From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

Journal: International journal of computer assisted radiology and surgery
PMID: 38642296

Abstract

PURPOSE: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.

Authors

  • Chris Yeung
    School of Computing, Queen's University, Kingston, ON, Canada. chris.yeung@queensu.ca.
  • Tamas Ungi
    Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada.
  • Zoe Hu
    School of Medicine, Queen's University, Kingston, ON, Canada.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Martin Kaufmann
    Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Ross Walker
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Shaila Merchant
    Department of Surgery, Queen's University, Ontario, Canada.
  • Cecil Jay Engel
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Doris Jabs
    Department of Radiology, Queen's University, Kingston, ON, Canada.
  • John Rudan
    Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Gabor Fichtinger
    Department of Mechanical and Material Engineering, Queen's University, Kingston, ON, Canada.

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