Summary
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).
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
| Data Type | Download all or Query/Filter | License |
|---|---|---|
| Images (DICOM, 7.55GB) | (Download requires the NBIA Data Retriever) | |
| DICOM Metadata Digest (CSV, 31 kB) | ||
| Lesion Notes (XLS, ) |
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.
- Imaging Data Commons (IDC) (Imaging 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:
- QIN multi-site collection of Lung CT data with Nodule Segmentations (QIN-LungCT-Seg)
- RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (RIDER-LungCT-Seg)
- QIBA VolCT Group 1B Round 2 No Change Size Measurements (QIBA-VolCT-1B)
Detailed Description
Radiology Imaging Statistics | |
|---|---|
Modalities | CT |
Number of Participants | 32 |
Number of Studies | 46 |
Number of Series | 63 |
Number of Images | 15,419 |
| Image Size (GB) | 7.55 |
Please contact TCIA's Helpdesk with any questions regarding usage.
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
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
Publication Citation
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
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 PMCID: PMC3824915
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.
Version 2 (Current): Updated 2014/11/14
| Data Type | Download all or Query/Filter |
|---|---|
| Images (DICOM, 7.55GB) | (Download requires the NBIA Data Retriever) |
| DICOM Metadata Digest (CSV) |
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.
Version 1: Updated 2012/10/18
Initial upload of data set.