Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels.

Journal: Computers in biology and medicine
PMID: 39637460

Abstract

BACKGROUND: A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these WSIs typically contain non-cancer tissue, introducing noise during model training. As digital pathology models typically start with splitting WSIs into tiles, we propose a model that can be used to exclude non-cancer tiles from the WSIs of lung squamous cell carcinoma (SqCC) tumours.

Authors

  • Salma Dammak
    Baines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada.
  • Matthew J Cecchini
    Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
  • Jennifer Coats
    Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
  • Katherina Baranova
    Deparment of Biochemistry , University of Western Ontario, London, Canada.
  • Aaron D Ward
    Department of Medical Biophysics, Western University and Lawson Health Research Institute, London, ON, Canada.

Keywords

External Resources

Popular Topics
Recent Journals