{"id":5582,"date":"2023-09-04T03:10:13","date_gmt":"2023-09-04T03:10:13","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/cc-radiomics-phantom-2\/"},"modified":"2023-09-13T11:58:23","modified_gmt":"2023-09-13T11:58:23","slug":"cc-radiomics-phantom-2","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/cc-radiomics-phantom-2\/","title":{"rendered":"CC-RADIOMICS-PHANTOM-2"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Phantom"],"citations":[4442,4443,2925],"collection_doi":"http:\/\/doi.org\/10.7937\/TCIA.2019.4l24tz5g","collection_download_info":"Click the Versions tab for more info about data releases.","collection_downloads":[4990],"full_export":"

Summary<\/h2>

This collection consists of 251 CT scans of Credence Cartridge Radiomic (CCR) phantom. This texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. This phantom dataset was acquired on 4-8 CT scanners using a set of imaging parameters (e.g., reconstruction Field of View, Slice thickness, reconstruction kernels, mAs, and Pitch). A controlled scanning approach was employed to assess the variability in radiomic features due to each imaging parameter. This dataset will be useful to radiomic research community to identify a subset of robust radiomic features and for establishing the ground truths for future clinical investigations.<\/span><\/p>

This Phantom dataset can be used for Feature variability assessment due to CT imaging parameters. These phantom scans can be used to identify a subset of robust radiomic features for future clinical investigations. Using this dataset, the numerical values of radiomic features can be cross-validated by other research groups using their own feature extraction tools.<\/span><\/p>

Acknowledgements<\/h3>

This dataset was submitted by Dr. Eduardo G. Moros and Dr. M Shafiq ul Hassan, USF. Special thanks to Moffitt Cancer Center where data were acquired.<\/span><\/p>