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A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter

Colorectal cancer is a major contributor to death and disease worldwide. The Apc(Min) mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenom...

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Autores principales: Shepherd, Amy L., Smith, A. Alexander T., Wakelin, Kirsty A., Kuhn, Sabine, Yang, Jianping, Eccles, David A., Ronchese, Franca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033248/
https://www.ncbi.nlm.nih.gov/pubmed/32080295
http://dx.doi.org/10.1038/s41598-020-60020-7
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author Shepherd, Amy L.
Smith, A. Alexander T.
Wakelin, Kirsty A.
Kuhn, Sabine
Yang, Jianping
Eccles, David A.
Ronchese, Franca
author_facet Shepherd, Amy L.
Smith, A. Alexander T.
Wakelin, Kirsty A.
Kuhn, Sabine
Yang, Jianping
Eccles, David A.
Ronchese, Franca
author_sort Shepherd, Amy L.
collection PubMed
description Colorectal cancer is a major contributor to death and disease worldwide. The Apc(Min) mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine of Apc(Min) mice, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias, and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study.
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spelling pubmed-70332482020-02-28 A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter Shepherd, Amy L. Smith, A. Alexander T. Wakelin, Kirsty A. Kuhn, Sabine Yang, Jianping Eccles, David A. Ronchese, Franca Sci Rep Article Colorectal cancer is a major contributor to death and disease worldwide. The Apc(Min) mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine of Apc(Min) mice, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias, and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study. Nature Publishing Group UK 2020-02-20 /pmc/articles/PMC7033248/ /pubmed/32080295 http://dx.doi.org/10.1038/s41598-020-60020-7 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Shepherd, Amy L.
Smith, A. Alexander T.
Wakelin, Kirsty A.
Kuhn, Sabine
Yang, Jianping
Eccles, David A.
Ronchese, Franca
A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter
title A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter
title_full A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter
title_fullStr A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter
title_full_unstemmed A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter
title_short A semi-automated technique for adenoma quantification in the Apc(Min) mouse using FeatureCounter
title_sort semi-automated technique for adenoma quantification in the apc(min) mouse using featurecounter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033248/
https://www.ncbi.nlm.nih.gov/pubmed/32080295
http://dx.doi.org/10.1038/s41598-020-60020-7
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