FACT: foundation model for assessing cancer tissue margins with mass spectrometry.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 4, 2025

Abstract

PURPOSE: Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass Spectrometry (REIMS), a tool for real-time intraoperative margin assessment, generates spectra that require machine learning models to support clinical decision-making. However, the scarcity of labeled data in surgical contexts presents a significant challenge. This study is the first to develop a foundation model tailored specifically for REIMS data, addressing this limitation and advancing real-time surgical margin assessment.

Authors

  • Mohammad Farahmand
    Queen's University, Kingston, ON, Canada. m.farahmand@queensu.ca.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Fahimeh Fooladgar
    Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
  • Laura Connolly
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Martin Kaufmann
    Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Kevin Yi Mi Ren
    Queen's University, Kingston, ON, Canada.
  • John Rudan
    Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada.
  • Doug McKay
    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.

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