Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning
Precision medicine, a popular treatment strategy, has become increasingly important to the development of targeted therapy. To correlate medical imaging with prognostic and genomic data, researches in radiomics and radiogenomics have provided many pre-defined image features to describe image informa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9395631/ |
| _version_ | 1819082471645380608 |
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| author | Dong Sui Maozu Guo Xiaoxuan Ma Julian Baptiste Lei Zhang |
| author_facet | Dong Sui Maozu Guo Xiaoxuan Ma Julian Baptiste Lei Zhang |
| author_sort | Dong Sui |
| collection | DOAJ |
| description | Precision medicine, a popular treatment strategy, has become increasingly important to the development of targeted therapy. To correlate medical imaging with prognostic and genomic data, researches in radiomics and radiogenomics have provided many pre-defined image features to describe image information quantitatively or qualitatively. However, in previous researches, there are only statistical results which prove high correlation among multi-source medical data, but those can’t give intuitive and visual result. In this paper, a deep learning based radiogenomic framework is provided to construct the linkage from lung tumor images to genomic data and implement generation process in turn, which form a bi-direction framework to map multi-source medical data. The imaging features are extracted from autoencoder under the condition of genomic data. It can obtain much more relevant features than traditional radiogenomic methods. Finally, we use generative adversarial network to transform genomic data onto tumor images, which gives a cogent result to explain the linkage between them. As a result, our framework provides a deep learning method to do radiogenomic researches more functionally and intuitively. |
| first_indexed | 2024-12-21T20:17:12Z |
| format | Article |
| id | doaj.art-2b516da2b89944d7b6c6b5cd425a45c3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T20:17:12Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2b516da2b89944d7b6c6b5cd425a45c32022-12-21T18:51:34ZengIEEEIEEE Access2169-35362021-01-01912524712525710.1109/ACCESS.2021.30714669395631Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep LearningDong Sui0https://orcid.org/0000-0002-7887-2111Maozu Guo1https://orcid.org/0000-0002-3899-1482Xiaoxuan Ma2Julian Baptiste3Lei Zhang4https://orcid.org/0000-0003-2990-5359School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaDiagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USADiagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USAPrecision medicine, a popular treatment strategy, has become increasingly important to the development of targeted therapy. To correlate medical imaging with prognostic and genomic data, researches in radiomics and radiogenomics have provided many pre-defined image features to describe image information quantitatively or qualitatively. However, in previous researches, there are only statistical results which prove high correlation among multi-source medical data, but those can’t give intuitive and visual result. In this paper, a deep learning based radiogenomic framework is provided to construct the linkage from lung tumor images to genomic data and implement generation process in turn, which form a bi-direction framework to map multi-source medical data. The imaging features are extracted from autoencoder under the condition of genomic data. It can obtain much more relevant features than traditional radiogenomic methods. Finally, we use generative adversarial network to transform genomic data onto tumor images, which gives a cogent result to explain the linkage between them. As a result, our framework provides a deep learning method to do radiogenomic researches more functionally and intuitively.https://ieeexplore.ieee.org/document/9395631/Radiomicsradiogenomicsdeep learninggenomics biomarkerGSEA |
| spellingShingle | Dong Sui Maozu Guo Xiaoxuan Ma Julian Baptiste Lei Zhang Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning IEEE Access Radiomics radiogenomics deep learning genomics biomarker GSEA |
| title | Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning |
| title_full | Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning |
| title_fullStr | Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning |
| title_full_unstemmed | Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning |
| title_short | Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning |
| title_sort | imaging biomarkers and gene expression data correlation framework for lung cancer radiogenomics analysis based on deep learning |
| topic | Radiomics radiogenomics deep learning genomics biomarker GSEA |
| url | https://ieeexplore.ieee.org/document/9395631/ |
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