You are here

Article Text

Article menu
Download PDFPDF
Original article
Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours
  1. Nina Linder1,2,
  2. Jenny C Taylor3,
  3. Richard Colling4,
  4. Robert Pell4,
  5. Edward Alveyn5,
  6. Johnson Joseph6,
  7. Andrew Protheroe6,
  8. Mikael Lundin1,
  9. Johan Lundin1,7,
  10. Clare Verrill8
  1. 1 Institute for Molecular Medicine Finland, HILIFE, University of Helsinki, Helsinki, Finland
  2. 2 Department of Women's and Children's Health, International Maternal and Child Health, Uppsala University, Uppsala, Sweden
  3. 3 Wellcome Trust Centre for Human Genetics, University of Oxford and Oxford NIHR Biomedical Research Centre, Oxford, UK
  4. 4 Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
  5. 5 University of Oxford, Medical School, Oxford, UK
  6. 6 Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
  7. 7 Department of Public Health Sciences, Global Health/IHCAR, Karolinska Institutet, Stockholm, Sweden
  8. 8 Nuffield Department of Surgical Sciences and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
  1. Correspondence to Dr Nina Linder, Institute for Molecular Medicine Finland - FIMM, University of Helsinki, Helsinki 00290, Finland; nina.linder{at}helsinki.fi

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.

Methods TILs were manually annotated in 259 tumour regions from 28 whole-slide images (WSIs) of H&E-stained tissue samples. A deep learning algorithm was trained on half of the regions and tested on the other half. The algorithm was further applied to larger areas of tumour WSIs from 89 patients and correlated with clinicopathological data.

Results A correlation coefficient of 0.89 was achieved when comparing the algorithm with the manual TIL count in the test set of images in which TILs were present (n=47). In the WSI regions from the 89 patient samples, the median TIL density was 1009/mm2. In seminomas, none of the relapsed patients belonged to the highest TIL density tertile (>2011/mm2). TIL quantifications performed visually by three pathologists on the same tumours were not significantly associated with outcome. The average interobserver agreement between the pathologists when assigning a patient into TIL tertiles was 0.32 (Kappa test) compared with 0.35 between the algorithm and the experts, respectively. A higher TIL density was associated with a lower clinical tumour stage, seminoma histology and lack of lymphovascular invasion.

Conclusions Deep learning–based image analysis can be used for detecting TILs in testicular germ cell cancer more objectively and it has potential for use as a prognostic marker for disease relapse.

  • digital pathology
  • testis
  • tumour immunity
  • image analysis

https://doi.org/10.1136/jclinpath-2018-205328

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    View Full Text

    Footnotes

    • Handling editor Dhirendra Govender.

    • Contributors CV, JL and NL conceived the concept of this study and supervised the study. JL and NL designed computational experiments. CV, JL and NL implemented the experiments. JL performed statistical analyses of the results. CV, NL and JL drafted the manuscript. CV collected the patient sample data and collected the clinical information. CV, RC, and RP performed the visual scorings. ML performed management and pre-processing of digital slides. JCT helped conceive the study and JJ collected the clinical data. All authors approved the manuscript.

    • Funding The research has received funding from the Sigrid Jusélius Foundation (JL), Medicinska Understödsföreningen Liv och Hälsa (JL), Stiftelsen Dorothea Olivia, Karl Walter och Jarl Walter Perkléns minne (NL) and Finska Läkaresällskapet (NL, JL). CV's research time is part funded by the Oxford NIHR Biomedical Research Centre (Molecular Diagnostics Theme/Multimodal Pathology Subtheme). The research was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC).

    • Disclaimer The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

    • Competing interests JL and ML are founders and consultants at Fimmic Oy, Helsinki, Finland.

    • Patient consent Not required.

    • Ethics approval This manuscript reports a retrospective study of routinely collected samples conducted under London-Westminster REC approval 14/LO/2074.

    • Provenance and peer review Not commissioned; externally peer reviewed.