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Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures
Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genom...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290166/ https://www.ncbi.nlm.nih.gov/pubmed/34295824 http://dx.doi.org/10.3389/fonc.2021.699265 |
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author | Liu, Dongming Chen, Jiu Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Ge, Honglin Zhang, Wenbin Liu, Hongyi |
author_facet | Liu, Dongming Chen, Jiu Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Ge, Honglin Zhang, Wenbin Liu, Hongyi |
author_sort | Liu, Dongming |
collection | PubMed |
description | Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management. |
format | Online Article Text |
id | pubmed-8290166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82901662021-07-21 Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures Liu, Dongming Chen, Jiu Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Ge, Honglin Zhang, Wenbin Liu, Hongyi Front Oncol Oncology Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management. Frontiers Media S.A. 2021-07-06 /pmc/articles/PMC8290166/ /pubmed/34295824 http://dx.doi.org/10.3389/fonc.2021.699265 Text en Copyright © 2021 Liu, Chen, Hu, Yang, Liu, Hu, Ge, Zhang and Liu 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 | Oncology Liu, Dongming Chen, Jiu Hu, Xinhua Yang, Kun Liu, Yong Hu, Guanjie Ge, Honglin Zhang, Wenbin Liu, Hongyi Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures |
title | Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures |
title_full | Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures |
title_fullStr | Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures |
title_full_unstemmed | Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures |
title_short | Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures |
title_sort | imaging-genomics in glioblastoma: combining molecular and imaging signatures |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290166/ https://www.ncbi.nlm.nih.gov/pubmed/34295824 http://dx.doi.org/10.3389/fonc.2021.699265 |
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