TCGA-OV-Proteogenomics | 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 | Page Accessibility: Public | Analysis Result
| Location | Subjects | Updated | |||
|---|---|---|---|---|---|
| Ovarian | Ovary | 20 | Radiologist assessments of image features, proteogenomic features | 04/15/2020 |
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 The Cancer Genome Atlas Ovarian Cancer Collection (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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Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:
| Title | Data Type | Format | Access Points | License | |||
|---|---|---|---|---|---|---|---|
| Images | DICOM | Requires NBIA Data Retriever |
CC BY 3.0 | ||||
| Image Analyses, Proteogenomic features, and Clinical data | CSV | CC BY 3.0 |
Citations & Data Usage Policy
Data Citation |
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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 (TCGA-OV-Proteogenomics) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.9stoinf1 |
Publication Citation |
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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 |
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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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 |
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