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.
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.
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.
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.
Journal of clinical pathology. 2018 Dec 05 [Epub]
Nina Linder, Jenny C Taylor, Richard Colling, Robert Pell, Edward Alveyn, Johnson Joseph, Andrew Protheroe, Mikael Lundin, Johan Lundin, Clare Verrill
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., University of Oxford, Medical School, Oxford, UK., Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK., Institute for Molecular Medicine Finland, HILIFE, University of Helsinki, Helsinki, Finland., Nuffield Department of Surgical Sciences and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.