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A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images

The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation gr...

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Detalles Bibliográficos
Autores principales: Liu, Liangliang, Wang, Ying, Chang, Jing, Zhang, Pei, Xiong, Shufeng, Liu, Hebing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227422/
https://www.ncbi.nlm.nih.gov/pubmed/37260749
http://dx.doi.org/10.1016/j.isci.2023.106874
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author Liu, Liangliang
Wang, Ying
Chang, Jing
Zhang, Pei
Xiong, Shufeng
Liu, Hebing
author_facet Liu, Liangliang
Wang, Ying
Chang, Jing
Zhang, Pei
Xiong, Shufeng
Liu, Hebing
author_sort Liu, Liangliang
collection PubMed
description The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation graph attention network (MLP-GAT) that can construct graphs for classifying multi-type CINs from the WSIs of breast cancer. We construct a WSIs dataset of breast cancer from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Extensive experiments show that MLP-GAT far outperforms accepted state-of-the-art methods and demonstrate the advantages of the constructed graph networks for analyzing WSI data. The visualization shows the difference among the tiles in a WSI. Furthermore, the generalization performance of the proposed method was verified on the stomach cancer. This study provides guidance for studying the relationship between CIN and cancer from the perspective of image phenotype.
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spelling pubmed-102274222023-05-31 A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images Liu, Liangliang Wang, Ying Chang, Jing Zhang, Pei Xiong, Shufeng Liu, Hebing iScience Article The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation graph attention network (MLP-GAT) that can construct graphs for classifying multi-type CINs from the WSIs of breast cancer. We construct a WSIs dataset of breast cancer from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Extensive experiments show that MLP-GAT far outperforms accepted state-of-the-art methods and demonstrate the advantages of the constructed graph networks for analyzing WSI data. The visualization shows the difference among the tiles in a WSI. Furthermore, the generalization performance of the proposed method was verified on the stomach cancer. This study provides guidance for studying the relationship between CIN and cancer from the perspective of image phenotype. Elsevier 2023-05-18 /pmc/articles/PMC10227422/ /pubmed/37260749 http://dx.doi.org/10.1016/j.isci.2023.106874 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Liu, Liangliang
Wang, Ying
Chang, Jing
Zhang, Pei
Xiong, Shufeng
Liu, Hebing
A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
title A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
title_full A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
title_fullStr A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
title_full_unstemmed A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
title_short A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
title_sort correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227422/
https://www.ncbi.nlm.nih.gov/pubmed/37260749
http://dx.doi.org/10.1016/j.isci.2023.106874
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