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Combining multiple spatial statistics enhances the description of immune cell localisation within tumours

Digital pathology enables computational analysis algorithms to be applied at scale to histological images. An example is the identification of immune cells within solid tumours. Image analysis algorithms can extract precise cell locations from immunohistochemistry slides, but the resulting spatial c...

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Autores principales: Bull, Joshua A., Macklin, Philip S., Quaiser, Tom, Braun, Franziska, Waters, Sarah L., Pugh, Chris W., Byrne, Helen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596100/
https://www.ncbi.nlm.nih.gov/pubmed/33122646
http://dx.doi.org/10.1038/s41598-020-75180-9
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author Bull, Joshua A.
Macklin, Philip S.
Quaiser, Tom
Braun, Franziska
Waters, Sarah L.
Pugh, Chris W.
Byrne, Helen M.
author_facet Bull, Joshua A.
Macklin, Philip S.
Quaiser, Tom
Braun, Franziska
Waters, Sarah L.
Pugh, Chris W.
Byrne, Helen M.
author_sort Bull, Joshua A.
collection PubMed
description Digital pathology enables computational analysis algorithms to be applied at scale to histological images. An example is the identification of immune cells within solid tumours. Image analysis algorithms can extract precise cell locations from immunohistochemistry slides, but the resulting spatial coordinates, or point patterns, can be difficult to interpret. Since localisation of immune cells within tumours may reflect their functional status and correlates with patient prognosis, novel descriptors of their spatial distributions are of biological and clinical interest. A range of spatial statistics have been used to analyse such point patterns but, individually, these approaches only partially describe complex immune cell distributions. In this study, we apply three spatial statistics to locations of CD68+ macrophages within human head and neck tumours, and show that images grouped semi-quantitatively by a pathologist share similar statistics. We generate a synthetic dataset which emulates human samples and use it to demonstrate that combining multiple spatial statistics with a maximum likelihood approach better predicts human classifications than any single statistic. We can also estimate the error associated with our classifications. Importantly, this methodology is adaptable and can be extended to other histological investigations or applied to point patterns outside of histology.
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spelling pubmed-75961002020-10-30 Combining multiple spatial statistics enhances the description of immune cell localisation within tumours Bull, Joshua A. Macklin, Philip S. Quaiser, Tom Braun, Franziska Waters, Sarah L. Pugh, Chris W. Byrne, Helen M. Sci Rep Article Digital pathology enables computational analysis algorithms to be applied at scale to histological images. An example is the identification of immune cells within solid tumours. Image analysis algorithms can extract precise cell locations from immunohistochemistry slides, but the resulting spatial coordinates, or point patterns, can be difficult to interpret. Since localisation of immune cells within tumours may reflect their functional status and correlates with patient prognosis, novel descriptors of their spatial distributions are of biological and clinical interest. A range of spatial statistics have been used to analyse such point patterns but, individually, these approaches only partially describe complex immune cell distributions. In this study, we apply three spatial statistics to locations of CD68+ macrophages within human head and neck tumours, and show that images grouped semi-quantitatively by a pathologist share similar statistics. We generate a synthetic dataset which emulates human samples and use it to demonstrate that combining multiple spatial statistics with a maximum likelihood approach better predicts human classifications than any single statistic. We can also estimate the error associated with our classifications. Importantly, this methodology is adaptable and can be extended to other histological investigations or applied to point patterns outside of histology. Nature Publishing Group UK 2020-10-29 /pmc/articles/PMC7596100/ /pubmed/33122646 http://dx.doi.org/10.1038/s41598-020-75180-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Bull, Joshua A.
Macklin, Philip S.
Quaiser, Tom
Braun, Franziska
Waters, Sarah L.
Pugh, Chris W.
Byrne, Helen M.
Combining multiple spatial statistics enhances the description of immune cell localisation within tumours
title Combining multiple spatial statistics enhances the description of immune cell localisation within tumours
title_full Combining multiple spatial statistics enhances the description of immune cell localisation within tumours
title_fullStr Combining multiple spatial statistics enhances the description of immune cell localisation within tumours
title_full_unstemmed Combining multiple spatial statistics enhances the description of immune cell localisation within tumours
title_short Combining multiple spatial statistics enhances the description of immune cell localisation within tumours
title_sort combining multiple spatial statistics enhances the description of immune cell localisation within tumours
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596100/
https://www.ncbi.nlm.nih.gov/pubmed/33122646
http://dx.doi.org/10.1038/s41598-020-75180-9
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