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The Cancer Imaging Archive

DRO-Toolkit | Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

DOI: 10.7937/T062-8262 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Phantom Human 32 SEG, CT Phantom 5.46GB Public, Complete 2020/04/09


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

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Segmentations SEG, CT DICOM
Download requires NBIA Data Retriever
32 32 64 9,632 CC BY 3.0
Images and Segmentations ZIP and NIFTI 64 CC BY 3.0
Analysis Results Using This Collection

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.

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)
  • 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

    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.

    Detailed Description

    The detailed description table applies to the DICOM files only. The NIfTI data are an additional 64 files, 84.21 MB.


    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)

    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.

    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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC.


    • 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

    Analysis Results Using this Collection

    TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: