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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
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Objectives
To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).
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Methods
This retrospective
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, hypothesis-
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generating study
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included 20 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
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texture features were computed from each
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tumour site. Three texture features
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that represented intra-and inter-site
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tumour heterogeneity were used for
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analysis.
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An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumour sites and metastasis.
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Correlations between protein-abundance and various CT imaging traits and texture features
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were assessed using the Kendall tau rank correlation-coefficient and the Mann-Whitney U test,
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whereas the area under the receiver-operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association.
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p values < 0.05 were considered significant.
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RESULTS:
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Results
Four proteins were
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associated with CT-based imaging traits
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, with the strongest correlation observed between the CRIP2 protein and disease in the
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mesentery (p<0.001, AUC=0.
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05).
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The abundance of three proteins was associated with texture features
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that represented intra-and inter-site
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tumour heterogeneity, with the strongest
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negative correlation
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between the CKB protein
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and
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cluster dissimilarity
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(p=0.047, 𝜏 =0.326).
Conclusion
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This study provides the first insights
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into the potential associations between standard-of-care CT imaging traits and
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texture measures of
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intra-and inter-site heterogeneity, and the abundance of several
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proteins.
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Coming soon |
Download
- DICOM Image Data: 20 patient subset of TCGA-OV
- Click the Download button above to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever.
- Image Features: (Summary table provided in publication)
- 36 image features, including the 33 radiologists-scored imaging traits used in the study of Vargas et al such as lesion size and laterality, locations of peritoneal disease, nodal stations involved, and locations of metastases, and three computer-extracted texture metrics were obtained.
- Segmentations
- Segmentation was performed using 3DSlicer by tracing the contour of each lesion on each slice to produce the volume of interests (VOI). Voxel-wise Haralick textures (energy, entropy, contrast, and homogeneity) were computed from within the manually delineated VOIs using in-house software implemented in C++ using the Insight ToolKit. Site specific sub-regions were computed by voxel-wise clustering of the Haralick textures using kernel K-means method. Following clustering, tumor sites were divided into distinct sub-regions and summarized using average of Haralick texture measures of all voxels within that region.
- Analyses, Proteogenomic features, and Clinical data:
- Image Segmentation Labels: Coming soonProteomic DataProtein relative abundance measurements (link to paper and/or provide spreadsheet of data used)