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  • Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features (DRO-Toolkit)


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.


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 TypeDownload all or Query/FilterLicence

Images and Segmentations (DICOM, 5.0 GB)

Images and Segmentations (NIfTI, zip, 84.21 MB)

Click the Versions tab for more info about data releases.

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.

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



Number of Participants


Number of Studies


Number of Series


Number of Images


Images Size (GB)5.0 GB

The detailed description table applies to the DICOM files only. The NIfTI data is not included in this table.

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.

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.

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:

Grant Citations

  • 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 TypeDownload all or Query/Filter

Images and Segmentations (DICOM, 5.0 GB)

Images (NIfTI, zip)

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