Summary
Acknowledgements
We would like to acknowledge the individuals and institutions that contributed to the development and creation of these digital reference objects:
- Stanford University School of Medicine, Stanford, California, USA - Akshay Jaggi B.S. and Sandy Napel PhD from the Department of Radiology
- University of California, Los Angeles School of Medicine, Los Angeles, California, USA - Michael McNitt-Gray PhD from the Department of Radiology
- The University of Western Ontario, Department of Medical Biophysics - Sarah Mattonen PhD
- The National Cancer Institute Quantitative Imaging Network (QIN)
Data Access
Data Type | Download all or Query/Filter | Licence |
---|---|---|
Images and Segmentations (DICOM, 5.0 GB) | (Requires NBIA Data Retriever .) | |
Images and Segmentations (*.nii, ZIP, 64 files, 84.21 MB) |
Additional Resources for this Dataset
The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.
- https://github.com/riipl/dro_cli (Software toolkit for the creation of Digital Reference Objects)
Additional Resources for this Dataset
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
- Imaging Data Commons (IDC) (Imaging Data)
Third Party Analyses of this Dataset
TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:
Detailed Description
Image Statistics | |
---|---|
Modalities | CT, SEG |
Number of Participants | 32 |
Number of Studies | 32 |
Number of Series | 64 |
Number of Images | 9632 |
Images Size (GB) | 5.0 GB |
The detailed description table applies to the DICOM files only. The NIfTI data are an additional 64 files, 84.21 MB.
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
Jaggi, A., Mattonen, S. A., McNitt-Gray, M., & Napel, S. (2020). Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/T062-8262
Publication Citation
Jaggi, A., Mattonen, S. A., McNitt-Gray, M., & Napel, S. (2020). Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. In Tomography (Vol. 6, Issue 2, pp. 111–117). MDPI AG. https://doi.org/10.18383/j.tom.2019.00030
TCIA Citation
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. DOI: https://doi.org/10.1007/s10278-013-9622-7
Acknowledgement
- David Geffen School of Medicine at UCLA - U01CA181156
- Stanford University School of Medicine – U01CA187947 and U24CA180927
- University of Michigan - U01CA232931
- University of Washington – R50CA211270, U01CA148131
- University of South Florida - U24CA180927, U01CA200464
- Moffitt Cancer Center – U01CA143062, U01CA200464, P30CA076292
- UC San Francisco - U01CA225427
- BC Cancer Research Centre - NSERC Discovery Grant: RGPIN-2019-06467
- Columbia University- U01CA225431
- Center for Biomedical Image Computing and Analytics at the University of Pennsylvania - U24CA189523, R01NS042645
- Massachusetts General Hospital- U01CA154601, U24CA180927
Other Publications Using This Data
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
Version 1 (Current): Updated 2020/04/09
Data Type | Download all or Query/Filter |
---|---|
Images and Segmentations (DICOM, 5.0 GB) |
(Requires NBIA Data Retriever .) |
Images (NIfTI, zip) |