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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10227422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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