Child pages
  • Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis
Skip to end of metadata
Go to start of metadata

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

Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever

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

(Requires  NBIA Data Retriever )

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

Note:   Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description


Citations & Data Usage Policy 

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

Data Citation

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

Publication Citation

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

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. 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)

Other Publications Using This Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

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)


  • No labels