Summary
| Excerpt |
|---|
The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). |
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.
Acknowledgements
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.
| Redirect |
|---|
| delay | 5 |
|---|
| location | https://www.cancerimagingarchive.net/collection/cc-radiomics-phantom-2/ |
|---|
|
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.
Acknowledgements
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.
| Localtab Group |
|---|
| Localtab |
|---|
| active | true |
|---|
| title | Data Access |
|---|
| Data Access |
|
...
| Data Type | Download all or Query/Filter | License |
|---|
| Images (DICOM, |
|
|
...
...
| 5GB) |
| Tcia button generator |
|---|
| url | https://wiki.cancerimagingarchive.net/download/attachments/39879218/CC-Radiomics-Phantom-2-NBIA-manifest.tcia?version=1&modificationDate=1551287978550&api=v2 |
|---|
|
|
| Tcia button generator |
|---|
| label | Search |
|---|
| url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CC-Radiomics-Phantom-2 |
|---|
|
|
(Download requires the NBIA Data Retriever) | |
Additional Resources for this Dataset |
|
...
| Localtab |
|---|
| title | Detailed Description |
|---|
| Detailed Description |
|
...
...
...
...
...
...
...
...
GDC Data Portal - Clinical and Genomic Data
The GDC Data Portal has extensive clinical and genomic data, which can be matched to the patient identifiers on the images here in TCIA. Below is a snapshot of clinical data extracted on 1/27/2016.
Explanations of the clinical data can be found on the Biospecimen Core Resource Clinical Data Forms linked below:
A Note about TCIA and TCGA Subject Identifiers and Dates
Subject Identifiers: a subject with radiology images stored in TCIA is identified with a Patient ID that is identical to the Patient ID of the same subject with demographic, clinical, pathological, and/or genomic data stored in TCGA. For each TCGA case, the baseline TCGA imaging studies found on TCIA are pre-surgical.
Dates: TCIA and TCGA handle dates differently, and there are no immediate plans to reconcile:
...
| Localtab |
|---|
| title | Citations & Data Usage Policy |
|---|
| Citations & Data Usage Policy |
|
...
| Tcia limited license policy |
|---|
|
|
...
Shafiq ul Hassan M, Zhang G, Latifi K, Ullah G, Gillies R, Moros E. Credence Cartridge Radiomics Phantom CT Scans with Controlled Scanning Approach. 2018. DOI: http://doi.org/10.7937/ |
|
|
...
| Info |
|---|
| title | Publication Citation |
|---|
| Muhammad Shafiq ul Hassan, Geoffrey Zhang, Kujtim Latifi, Ghanim Ullah, Robert Gillies, Eduardo G. Moros. (2019) Computed Tomography Texture Phantom Dataset for Evaluating the Impact of CT Imaging Parameters on Radiomic Features. (link to attached PDF) |
| Info |
|---|
| 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. |
|
|
...
Additional Publication Resources:The Collection authors suggest the below will give context to this dataset: Shafiq ul Hassan M, Latifi K, Zhang G, Ullah G, Gillies R and Moros E. (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer patients. Scientific Reports. Shafiq ul Hassan M, Zhang G, Hunt D, Latifi K, Ullah G, Gillies R and Moros E, ‘Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra’, J. Med. Imag. 5(1), 011013 (2017). DOI: 10.1117/1.JMI.5.1.011013 Shafiq ul Hassan M, Zhang G, Latifi K, Ullah G, Hunt D, Balagurunathan Y, Abdullah M, Schabath M, Goldgof D, Mackin D, Court L, Gillies R and Moros E. (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med. Phys. 44(3), p-1050-1062 . Paul R, Shafiq ul Hassan M, Moros E, Gillies R, Hall L, Goldgof D. (2018) Stability of deep features across CT scanners and Field Of View (FOV) using a physical phantom. Proc SPIE Medical Imaging Conference, February 2018, Texas, USA
Other Publications Using This DataTCIA maintains a list of publications which leverage our |
|
...
data. If you have a manuscript you'd like to add please contact |
|
...
...
...
...
...
27| Data Type | Download all or Query/Filter |
|---|
| Images (DICOM, |
|
|
...
...
...
Added 9 new subjects of imaging data.
Version 5: Updated 2017/01/31
...
Added 6 new subjects of imaging data.
Version 4: Updated 2016/08/31
...
Added 20 subjects' imaging data.
Version 3: Updated 2016/05/31
...
Added 31 new subjects of imaging data.
Version 2: Updated 2016/01/27
...
Extracted latest release of clinical data (TXT) from the GDC Data Portal.
Version 1: Updated 2014/12/09
| Tcia button generator |
|---|
| url | https://wiki.cancerimagingarchive.net/download/attachments/39879218/CC-Radiomics-Phantom-2-NBIA-manifest.tcia?version=1&modificationDate=1551287978550&api=v2 |
|---|
|
|
| Tcia button generator |
|---|
| label | Search |
|---|
| url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CC-Radiomics-Phantom-2 |
|---|
|
|
|
|
...