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 Type Download all or Query/Filter Images (DICOM, 3 GB) (Requires  NBIA Data Retriever ) Image Analyses, Proteogenomic features, and Clinical data (CSV) Image Segmentation Labels Coming Soon Note:   Please contact help@cancerimagingarchive.net  with any questions regarding usage. Detailed Description Citations & Data Usage Policy  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 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 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 Type Download all or Query/Filter Images (DICOM, 3 GB) (Requires  NBIA Data Retriever ) Image Analyses, Proteogenomic features, and Clinical data (CSV) Image Segmentation Labels Coming Soon