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DOI: 10.7937/k9/tcia.2015.ofip7tvm | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Lung Human 244 PT, CT Lung Cancer 83.27GB Public, Complete 2015/12/29


 The RIDER Lung PET-CT collection was shared to facilitate the RIDER PET/CT subgroup activities. The PET/CT subgroup was responsible for: (1) archiving de-identified DICOM serial PET/CT phantom and lung cancer patient data in a public database to provide a resource for the testing and development of algorithms and imaging tools used for assessing response to therapy, (2) conducting multiple serial imaging studies of a long half-life phantom to assess systemic variance in serial PET/CT scans that is unrelated to response, and (3) identifying and recommending methods for quantifying sources of variance in PET/CT imaging with the goal of defining the change in PET measurements that may be unrelated to response to therapy, thus defining the absolute minimum effect size that should be used in the design of clinical trials using PET measurements as end points.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy.  The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

Data Access

Version 2: Updated 2015/12/29

It was brought to our attention that RIDER-1817358092 and RIDER-2617411955 appeared to be the same patient.  We have gone back to University of Washington and confirmed this is to be true.  RIDER-1817358092 has been removed as RIDER-2617411955 contained a couple additional series that were absent from the patient ID we removed.

Title Data Type Format Access Points Subjects Studies Series Images License
Download requires NBIA Data Retriever
243 274 1,328 266,280 CC BY 3.0
DICOM Metadata Digest CSV 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.

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

Muzi P, Wanner M, & Kinahan P. (2015). Data From RIDER Lung PET-CT. The Cancer Imaging Archive.

Other Publications Using this Data

TCIA maintains a list of publications which leverage our data. If you have a publication you’d like to add please contact TCIA’s Helpdesk.

  • Barani, R., & Sumathi, M. (2016). A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(11), 271-282. doi:10.14257/ijsip.2016.9.11.25
  • Desseroit, M.-C., Visvikis, D., Tixier, F., Majdoub, M., Perdrisot, R., Guillevin, R., . . . Hatt, M. (2016). Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III. European Journal of Nuclear Medicine and Molecular Imaging, 1-9. doi:10.1007/s00259-016-3325-5
  • Erkoc, M., & Icer, S. (2022). Analysis of Computed Tomography Images of Lung Cancer Patients with The Marker Controlled Based Method. Paper presented at the 2022 Medical Technologies Congress (TIPTEKNO), Antalya, Türkiye.
  • Gsaxner, C., Roth, P. M., Wallner, J., & Egger, J. (2019). Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data. PLoS One, 14(3), e0212550. doi:10.1371/journal.pone.0212550
  • Jin, H., & Kim, J. H. (2020). Evaluation of Feature Robustness Against Technical Parameters in CT Radiomics: Verification of Phantom Study with Patient Dataset. Journal of Signal Processing Systems, 92(3), 277-287. doi:
  • Si, H., Hao, X., Zhang, L., Xu, X., Cao, J., Wu, P., . . . Li, S. (2021). Total Lesion Glycolysis Estimated by a Radiomics Model From CT Image Alone. Front Oncol, 11, 664346. doi:10.3389/fonc.2021.664346
  • Thomas, R., Schalck, E., Fourure, D., Bonnefoy, A., & Cervera-Marzal, I. (2021). 2Be3-Net: Combining 2D and 3D Convolutional Neural Networks for 3D PET Scans Predictions. Paper presented at the International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021).
  • Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi:10.3389/fonc.2021.637804
  • Zheng, C., Wang, X., & Feng, D. (2015). A statistical method for lung tumor segmentation uncertainty in PET images based on user inference. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE.

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.

Previous Versions

Version 1: Updated 2011/09/14

Initial upload of data set.

Title Data Type Format Access Points Studies Series Images License