uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH), International Child Health and Nutrition. Institute for Molecular Medicine Finland, HILIFE, University of Helsinki, Helsinki, Finland .
Wellcome Trust Centre for Human Genetics, University of Oxford and Oxford NIHR Biomedical Research Centre, Oxford, UK .
Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK .
Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK .
Show others and affiliations
2018 (English)In: Journal of Clinical Pathology, ISSN 0021-9746, E-ISSN 1472-4146, Vol. 72, no 2, p. 157-164Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 72, no 2, p. 157-164
Keywords [en]
digital pathology, image analysis, testis, tumour immunity
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:uu:diva-369732DOI: 10.1136/jclinpath-2018-205328ISI: 000461572700010PubMedID: 30518631OAI: oai:DiVA.org:uu-369732DiVA, id: diva2:1271293
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-04-10Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Authority records BETA

Linder, Nina

Search in DiVA

By author/editor
Linder, Nina
By organisation
International Child Health and Nutrition
In the same journal
Journal of Clinical Pathology
Cancer and Oncology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 64 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf