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Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.
CIP TCGA Radiology Initiative
Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Bladder Phenotype Research Group.
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- University of North Carolina- Special thanks to J. Keith Smith, M.D., Ph.D. and Shanah Kirk from the Department of Radiology.
- Barretos Cancer Hospital, Barretos, São Paulo, Brazil – Special Thanks to Fabiano Rubião Lucchesi, MD and Natália Del Angelo Aredes
- University of Chicago- Special thanks to Nicholas Gruszauskas, Ph.D.
- University of Sheffield - Special thanks to James Catto, MB, ChB, PhD, FRCS from the Department of Oncology.
- Memorial Sloan-Kettering Cancer Center, New York, NY - Special thanks to Hebert A. Vargas Alvarez, MD and Pierre Elnajjar.
- Lahey Hospital & Medical Center, Burlington, MA - Special thanks to John Lemmerman, RT and Kimberly Reiger-Christ, PhD, Cancer Research, Sophia Gordon Cancer Center.
- University of Southern California- Special thanks to Siamak Daneshmand, MD, from the Department of Urology and Vinay Duddalwar, MD, FRCR from the Department of Radiology.
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.
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.
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.