Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.
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% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 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' visual assessment.
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
Riku
R
Turkki
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
Nina
N
Linder
Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland.
Panu E
PE
Kovanen
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
Teijo
T
Pellinen
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Department of Public Health Sciences/Global Health (IHCAR), Karolinska Institutet, Stockholm, Sweden.
Johan
J
Lundin
breast cancer; convolutional neural network; digital pathology; tumor microenvironment; tumor-infiltrating immune cells
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