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location | https://www.cancerimagingarchive.net/collection/dro-toolkit/ |
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This is a sample collection of synthetic 3D Digital Reference Objects (DROs) intended for standardization of quantitative imaging feature extraction pipelines. We have developed a software toolkit for the creation of DROs with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. This collection includes objects with a range of values for the various feature categories and many combinations of these categories.
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We would like to acknowledge the individuals and institutions
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Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
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)
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title | Data Access |
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Type | Download all or Query/Filter | Licence |
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Images and Segmentations (DICOM, |
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url | https://wiki.cancerimagingarchive.net/download/attachments/70222146/DRO%20Toolkit%20April%202020%20TCIA-manifest.tcia?version=1&modificationDate=1586449917460&api=v2 |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=DRO-Toolkit |
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(Requires NBIA Data Retriever .) | | Images and Segmentations (*.nii, ZIP, 64 files, 84.21 MB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/70222146/Stanford%20DRO%20Toolkit-niftis.zip?version=1&modificationDate=1584697627383&api=v2 |
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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. Nci_crdc additional resources |
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TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: |
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title | Detailed Description |
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Participants | 32 | Number of Studies | 32 | Number of Series | 64 | Number of Images | 9632 | Images Size (GB) |
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The detailed description table applies to the DICOM files only. The NIfTI data are an additional 64 files, 84.21 MB. |
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title | Citations & Data Usage Policy |
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McNitt-Gray, M., & Napel, S |
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. (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 |
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M McNitt-Gray, S Napel, A Jaggi, SA Mattonen , L Hadjiiski, M Muzi, D Goldgof, Y Balagurunathan, LA Pierce , PE Kinahan, EF Jones, A Nguyen, A Virkud, H-P Chan, N Emaminejad, M Wahi-Anwar, M Daly, M Abdalah, H Yang, L Lu, W Lv, A Rahmim, A Gastounioti, S Pati, S Bakas, D Kontos, B Zhao, J Kalpathy-Cramer, K Farahani. Standardization in Quantitative Imaging: A Multi-center Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Datasets. Accepted for publication, Tomography, Feb. 2020.
| Jaggi, A., Mattonen, S. A., McNitt-Gray, M., & Napel, S. (2020) |
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. Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features |
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Info |
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| 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 |
Info |
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| - 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
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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. |
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Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, | |
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url | https://wiki.cancerimagingarchive.net/download/attachments/70222146/DRO%20Toolkit%20April%202020%20TCIA-manifest.tcia?version=1&modificationDate=1586449917460&api=v2 |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=DRO-Toolkit |
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url | https://wiki.cancerimagingarchive.net/download/attachments/70222146/Stanford%20DRO%20Toolkit-niftis.zip?version=1&modificationDate=1584697627383&api=v2 |
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