Journal of Pathology Informatics

RESEARCH ARTICLE
Year
: 2016  |  Volume : 7  |  Issue : 1  |  Page : 38-

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples


Riku Turkki1, Nina Linder1, Panu E Kovanen2, Teijo Pellinen1, Johan Lundin3 
1 Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
2 Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland
3 Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Department of Public Health Sciences/Global Health (IHCAR), Karolinska Institutet, Stockholm, Sweden

Correspondence Address:
Riku Turkki
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki
Finland

Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Methods: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. Results: In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (k = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (k = 0.78). Conclusions: Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists«SQ» visual assessment.


How to cite this article:
Turkki R, Linder N, Kovanen PE, Pellinen T, Lundin J. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.J Pathol Inform 2016;7:38-38


How to cite this URL:
Turkki R, Linder N, Kovanen PE, Pellinen T, Lundin J. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J Pathol Inform [serial online] 2016 [cited 2022 Jul 5 ];7:38-38
Available from: https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=38;epage=38;aulast=Turkki;type=0