Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Xiao, Wei, Jiang, Yanyun, Yao, Zhigang, Zhou, Xiaoming, Sui, Xiaodan, Zheng, Yuanjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784772572372533248
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
work_keys_str_mv AT xiaowei ladgcnautomaticdiagnosticframeworkforquantitativeestimationofgrowthpatternsduringclinicalevaluationoflungadenocarcinoma
AT jiangyanyun ladgcnautomaticdiagnosticframeworkforquantitativeestimationofgrowthpatternsduringclinicalevaluationoflungadenocarcinoma
AT yaozhigang ladgcnautomaticdiagnosticframeworkforquantitativeestimationofgrowthpatternsduringclinicalevaluationoflungadenocarcinoma
AT zhouxiaoming ladgcnautomaticdiagnosticframeworkforquantitativeestimationofgrowthpatternsduringclinicalevaluationoflungadenocarcinoma
AT suixiaodan ladgcnautomaticdiagnosticframeworkforquantitativeestimationofgrowthpatternsduringclinicalevaluationoflungadenocarcinoma
AT zhengyuanjie ladgcnautomaticdiagnosticframeworkforquantitativeestimationofgrowthpatternsduringclinicalevaluationoflungadenocarcinoma