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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...

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Autores principales: Turkki, Riku, Linder, Nina, Kovanen, Panu E., Pellinen, Teijo, Lundin, Johan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5027738/
https://www.ncbi.nlm.nih.gov/pubmed/27688929
http://dx.doi.org/10.4103/2153-3539.189703
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author Turkki, Riku
Linder, Nina
Kovanen, Panu E.
Pellinen, Teijo
Lundin, Johan
author_facet Turkki, Riku
Linder, Nina
Kovanen, Panu E.
Pellinen, Teijo
Lundin, Johan
author_sort Turkki, Riku
collection PubMed
description 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.
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spelling pubmed-50277382016-09-29 Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples Turkki, Riku Linder, Nina Kovanen, Panu E. Pellinen, Teijo Lundin, Johan J Pathol Inform Research Article 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. Medknow Publications & Media Pvt Ltd 2016-09-01 /pmc/articles/PMC5027738/ /pubmed/27688929 http://dx.doi.org/10.4103/2153-3539.189703 Text en Copyright: © 2016 Journal of Pathology Informatics http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
spellingShingle Research Article
Turkki, Riku
Linder, Nina
Kovanen, Panu E.
Pellinen, Teijo
Lundin, Johan
Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
title Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
title_full Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
title_fullStr Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
title_full_unstemmed Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
title_short Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
title_sort antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5027738/
https://www.ncbi.nlm.nih.gov/pubmed/27688929
http://dx.doi.org/10.4103/2153-3539.189703
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