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  • Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis (TCGA-OV-Proteogenomics)
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Summary

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.


Data Access

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Data TypeDownload all or Query/Filter
Images (DICOM, 3 GB)

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Image Analyses, Proteogenomic features, and Clinical data (CSV)

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Collections Used in this Third Party Analysis
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Detailed Description


Citations & Data Usage Policy 

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:

Data Citation

Beer, L., Sahin, H., Blazic, I., Vargas, H. A., Veeraraghavan, H., Kirby, J., Fevrier-Sullivan, B., Freymann, J., Jaffe, C., Conrads, T., Maxwell, G., Darcy, K., Huang, E., & Sala, E. (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. [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.9stoinf1

Publication Citation

Beer, L., Sahin, H., Bateman, N. W., Blazic, I., Vargas, H. A., Veeraraghavan, H., Kirby, J., Fevrier-Sullivan, B., Freymann, J. B., Jaffe, C. C., Brenton, J., Miccó, M., Nougaret, S., Darcy, K. M., Maxwell, G. L., Conrads, T. P., Huang, E., & Sala, E. (2020). Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis. European Radiology, 30(8), 4306–4316. https://doi.org/10.1007/s00330-020-06755-3

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

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Version 1 (Current): 2020/04/06

Data TypeDownload all or Query/Filter
Images (DICOM, 3 GB)

(Requires  NBIA Data Retriever )

Image Analyses, Proteogenomic features, and Clinical data (CSV)


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