Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours.

Journal: Journal of clinical pathology
Published Date: Feb 1, 2019

Abstract

AIMS: To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient.

Authors

  • Nina Linder
    Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
  • Jenny C Taylor
    Wellcome Trust Centre for Human Genetics, University of Oxford and Oxford NIHR Biomedical Research Centre, Oxford, UK.
  • Richard Colling
    Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Robert Pell
    Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Edward Alveyn
    University of Oxford, Medical School, Oxford, UK.
  • Johnson Joseph
    Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Andrew Protheroe
    Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Mikael Lundin
    Institute for Molecular Medicine Finland, HILIFE, University of Helsinki, Helsinki, Finland.
  • Johan Lundin
    Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
  • Clare Verrill
    Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. Electronic address: Clare.Verrill@ouh.nhs.uk.

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