The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results
Open accessAbstract
PURPOSE: To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets. MATERIALS AND METHODS: A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available. RESULTS: There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance. CONCLUSION: This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.
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Cites
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064
Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-1
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Decoding global gene expression programs in liver cancer by noninvasive imaging
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KRAS Mutations in Non-Small Cell Lung Cancer
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Identification of noninvasive imaging surrogates for brain tumor gene-expression modules
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An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis.
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Cited by
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
“Quantitative imaging for personalized medicine is a recent field, with a limited number of publications 12,20–27 .”
BackgroundCT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges
Lubner, M. G., Smith, A. D., Sandrasegaran, K., Sahani, D. V., & Pickhardt, P. J. (2017) · RadioGraphics · doi:10.1148/rg.2017170056 · Cited by 773
Deep learning with multimodal representation for pancancer prognosis prediction
Cheerla, A., & Gevaert, O. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz342 · Cited by 421
Self-citationWang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.00986-2018 · Cited by 435
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Metadata
- Authors
- Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., Rubin, D. L., Napel, S., & Plevritis, S. K.
- Venue
- Radiology
- Published
- 2012-08
- Volume
- 264
- Issue
- 2
- Pages
- 387-396
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2166401924
- Cited by
- 398
- References
- 8
- FWCI
- 14.24
- Type
- journal article
- Language
- en
- Open access
- green
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- Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., Rubin, D. L., Napel, S., & Plevritis, S. K. (2012). Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results. Radiology. https://doi.org/10.1148/radiol.12111607
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