DRO-Toolkit | Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features
DOI: 10.7937/T062-8262 | Data Citation Required | 47 Views | 1 Citations | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
|---|---|---|---|---|---|---|---|---|
| Phantom | Human | 32 | SEG, CT, Segmentation | Phantom | Software/Source Code | Public, Complete | 2020/04/09 |
Summary
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.
Data Access
Version 1: Updated 2020/04/09
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images and Segmentations | SEG, CT | DICOM | Download requires NBIA Data Retriever |
32 | 32 | 64 | 9,632 | CC BY 3.0 |
| Images and Segmentations | Segmentation, CT | ZIP and NIFTI | 64 | CC BY 3.0 |
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)
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.
- Software toolkit for the creation of Digital Reference Objects: https://github.com/riipl/dro_cli
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
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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 |
Acknowledgement |
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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)
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication Citation |
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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 |
No other publications by dataset authors were recommended.
Research Community Publications
TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.