RIDER Lung CT | RIDER Lung CT
DOI: 10.7937/k9/tcia.2015.u1x8a5nr | Data Citation Required | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
|---|---|---|---|---|---|---|---|---|
| Chest | Human | 32 | CT | Lung Cancer | Image Analyses | Public, Complete | 2014/11/14 |
Summary
The RIDER Lung CT collection was constructed as part of a study to evaluate the variability of tumor unidimensional, bidimensional, and volumetric measurements on same-day repeat computed tomographic (CT) scans in patients with non–small cell lung cancer.
Thirty-two patients with non–small cell lung cancer, each of whom underwent two CT scans of the chest within 15 minutes by using the same imaging protocol, were included in this study. Three radiologists independently measured the two greatest diameters of each lesion on both scans and, during another session, measured the same tumors on the first scan. In a separate analysis, computer software was applied to assist in the calculation of the two greatest diameters and the volume of each lesion on both scans. Concordance correlation coefficients (CCCs) and Bland-Altman plots were used to assess the agreements between the measurements of the two repeat scans (reproducibility) and between the two repeat readings of the same scan (repeatability).
The reproducibility and repeatability of the three radiologists' measurements were high (all CCCs, ≥0.96). The reproducibility of the computer-aided measurements was even higher (all CCCs, 1.00). The 95% limits of agreements for the computer-aided unidimensional, bidimensional, and volumetric measurements on two repeat scans were (−7.3%, 6.2%), (−17.6%, 19.8%), and (−12.1%, 13.4%), respectively. Chest CT scans are well reproducible. Changes in unidimensional lesion size of 8% or greater exceed the measurement variability of the computer method and can be considered significant when estimating the outcome of therapy in a patient.
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 2014/11/14
It was brought to our attention that the RIDER-8509201188 patient contained 2 identical image series rather than the correct secondary/repeat series. The duplicate series has been removed (UID: 1.3.6.1.4.1.9328.50.1.64033480205396366773922006817138551096), but we are unable to obtain the correct series at this point.
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images | SEG, CT | DICOM | Download requires NBIA Data Retriever |
32 | 46 | 153 | 15,509 | CC BY 3.0 |
| DICOM Metadata Digest | CSV | CC BY 3.0 | ||||||
| Lesion Notes | XLS | 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)
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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Zhao, B., Schwartz, L. H., & Kris, M. G. (2015). Data From RIDER Lung CT (Version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/k9/tcia.2015.u1x8a5nr |
Other Publications Using this Data
TCIA maintains a list of publications which leverage our data.
- Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. DOI:10.18383/j.tom.2016.00235
- Textural Analysis of Tumour Imaging: A Radiomics Approach. https://lib.ugent.be/catalog/rug01:002367219
If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.
Publication Citation |
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Zhao, B., James, L. P., Moskowitz, C. S., Guo, P., Ginsberg, M. S., Lefkowitz, R. A., Qin, Y., Riely, G. J., Kris, M. G., & Schwartz, L. H. (2009). Evaluating Variability in Tumor Measurements from Same-day Repeat CT Scans of Patients with Non–Small Cell Lung Cancer. In Radiology (Vol. 252, Issue 1, pp. 263–272). Radiological Society of North America (RSNA). https://doi.org/10.1148/radiol.2522081593 |
TCIA Citation |
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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. (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. https://doi.org/10.1007/s10278-013-9622-7 |
Previous Versions
Version 1: Updated 2012/10/18
Initial upload of data set.
| Title | Data Type | Format | Access Points | License |
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