Summary
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Lucian Beer, Hilal Sahin, Ivana Blazic, Hebert Alberto Vargas, Harini Veeraraghavan, Justin Kirby, Brenda Fevrier-Sullivan, John Freymann, Carl Jaffe, Thomas Conrads, George Maxwell, Kathleen Darcy, Erich Huang, Evis Sala. (2019) Data from Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis. DOI: 10.7937/TCIA.2019.9stoinf1 |
Description
Objectives
To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).
Methods
This retrospective, hypothesis-generating study included 20 TCGA-OV patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features were computed from each tumour site. Three texture features that represented intra-and inter-site tumour heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumour sites and metastasis. Correlations between protein-abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation-coefficient and the Mann-Whitney U test, whereas the area under the receiver-operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. p values < 0.05 were considered significant.
Results
Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p<0.001, AUC=0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumour heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p=0.047, 𝜏 =0.326).
Conclusion
This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra-and inter-site heterogeneity, and the abundance of several proteins.
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| Image Analyses, Proteogenomic features, and Clinical data |
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| Detailed Description
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Public collection license |
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| Lucian Beer, Hilal Sahin, Ivana Blazic, Hebert Alberto Vargas, Harini Veeraraghavan, Justin Kirby, Brenda Fevrier-Sullivan, John Freymann, Carl Jaffe, Thomas Conrads, George Maxwell, Kathleen Darcy, Erich Huang, Evis Sala. (2019) Data from Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis. DOI: 10.7937/TCIA.2019.9stoinf1 |
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title | Publication Citation |
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| Beer, L., Sahin, H., Bateman, N.W. et al. Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis. Eur Radiol (2020). https://doi.org/10.1007/s00330-020-06755-3 |
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| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper) |
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| Version 1 (Current): 2020/04/06
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