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LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma
Quantitative estimation of growth patterns is important for diagnosis of lung adenocarcinoma and prediction of prognosis. However, the growth patterns of lung adenocarcinoma tissue are very dependent on the spatial organization of cells. Deep learning for lung tumor histopathological image analysis...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399647/ https://www.ncbi.nlm.nih.gov/pubmed/36035486 http://dx.doi.org/10.3389/fphys.2022.946099 |
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author | Xiao, Wei Jiang, Yanyun Yao, Zhigang Zhou, Xiaoming Sui, Xiaodan Zheng, Yuanjie |
author_facet | Xiao, Wei Jiang, Yanyun Yao, Zhigang Zhou, Xiaoming Sui, Xiaodan Zheng, Yuanjie |
author_sort | Xiao, Wei |
collection | PubMed |
description | Quantitative estimation of growth patterns is important for diagnosis of lung adenocarcinoma and prediction of prognosis. However, the growth patterns of lung adenocarcinoma tissue are very dependent on the spatial organization of cells. Deep learning for lung tumor histopathological image analysis often uses convolutional neural networks to automatically extract features, ignoring this spatial relationship. In this paper, a novel fully automated framework is proposed for growth pattern evaluation in lung adenocarcinoma. Specifically, the proposed method uses graph convolutional networks to extract cell structural features; that is, cells are extracted and graph structures are constructed based on histopathological image data without graph structure. A deep neural network is then used to extract the global semantic features of histopathological images to complement the cell structural features obtained in the previous step. Finally, the structural features and semantic features are fused to achieve growth pattern prediction. Experimental studies on several datasets validate our design, demonstrating that methods based on the spatial organization of cells are appropriate for the analysis of growth patterns. |
format | Online Article Text |
id | pubmed-9399647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93996472022-08-25 LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma Xiao, Wei Jiang, Yanyun Yao, Zhigang Zhou, Xiaoming Sui, Xiaodan Zheng, Yuanjie Front Physiol Physiology Quantitative estimation of growth patterns is important for diagnosis of lung adenocarcinoma and prediction of prognosis. However, the growth patterns of lung adenocarcinoma tissue are very dependent on the spatial organization of cells. Deep learning for lung tumor histopathological image analysis often uses convolutional neural networks to automatically extract features, ignoring this spatial relationship. In this paper, a novel fully automated framework is proposed for growth pattern evaluation in lung adenocarcinoma. Specifically, the proposed method uses graph convolutional networks to extract cell structural features; that is, cells are extracted and graph structures are constructed based on histopathological image data without graph structure. A deep neural network is then used to extract the global semantic features of histopathological images to complement the cell structural features obtained in the previous step. Finally, the structural features and semantic features are fused to achieve growth pattern prediction. Experimental studies on several datasets validate our design, demonstrating that methods based on the spatial organization of cells are appropriate for the analysis of growth patterns. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399647/ /pubmed/36035486 http://dx.doi.org/10.3389/fphys.2022.946099 Text en Copyright © 2022 Xiao, Jiang, Yao, Zhou, Sui and Zheng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Xiao, Wei Jiang, Yanyun Yao, Zhigang Zhou, Xiaoming Sui, Xiaodan Zheng, Yuanjie LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
title | LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
title_full | LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
title_fullStr | LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
title_full_unstemmed | LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
title_short | LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
title_sort | lad-gcn: automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399647/ https://www.ncbi.nlm.nih.gov/pubmed/36035486 http://dx.doi.org/10.3389/fphys.2022.946099 |
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