Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
PMID: 39561274

Abstract

PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predict outcome for osteosarcoma in the neoadjuvant setting.

Authors

  • Nicolas Coudray
    Applied Bioinformatics Laboratories, NYU Grossman School of Medicine, New York, NY, USA. nicolas.coudray@nyulangone.org.
  • Michael A Occidental
    Ronald O. Perelman Department of Dermatology, NYU Langone Grossman School of Medicine, New York, New York.
  • Jose G Mantilla
    Department of Pathology, NYU Langone Grossman School of Medicine, New York, New York.
  • Adalberto Claudio Quiros
    School of Computing Science, University of Glasgow, Glasgow, Scotland, UK.
  • Ke Yuan
    Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
  • Jan Balko
    Department of Pathology and Molecular Medicine, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
  • Aristotelis Tsirigos
    Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA; Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA. Electronic address: aristotelis.tsirigos@nyulangone.org.
  • George Jour
    Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York, NY, USA. George.Jour@nyulangone.org.

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