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RIDER-LUNG-CT - The Cancer Imaging Archive (TCIA)
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RIDER-LUNG-CT


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The Cancer Imaging Archive

RIDER Lung CT | Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters

DOI: 10.7937/k9/tcia.2015.u1x8a5nr | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Chest Human 32 CT, SEG Lung Cancer 43.01GB Image Analyses Public, Complete 2024/06/25

Summary

Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Unlike biopsy-based biomarkers, QIBs are non-invasive and can estimate the spatial and temporal heterogeneity of total tumor burden. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility. 

In recent years, despite overwhelmingly increased awareness of the reproducibility and robustness in quantitative imaging studies, lack of precious clinical image data limits our investigation and algorithm development. Here, we contribute to the cancer imaging community with our investigator-initiated, same-day repeat CT scan images of 32 non–small cell lung cancer (NSCLC) patients, along with radiologist’s annotated lesion contours as the reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. One of the 6-settings, i.e., the setting of 1.25mm slice thickness and lung reconstruction (1.25L), was published as part of the Reference Image Database to Evaluate Therapy Response (RIDER) project in 2012. 

We believe that this entire dataset, comprising CT lung cancer images reconstructed on the same day at six different image settings, holds considerable value for advancing the development of robust Artificial Intelligence (AI) and machine learning (ML) methods. Additionally, it provides a valuable resource for comparing QIBs derived from a wide range of CT imaging parameter settings, for investigating data harmonization approaches, and for identifying specific CT imaging parameters most suitable for studying radiomics in lung cancer.

Design Type(s)

database creation objective • data integration objective • image analysis objective

Measurement Type(s)

non-small cell lung carcinoma

Technology Type(s)

computed tomography scanner • image segmentation

Factor Type(s)

repeat scans • image reconstruction settings

Sample Characteristic(s)

Homo sapiens • lung

 

Data Access

Version 3: Updated 2024/06/25

Added new CT and SEG series for all patients.

Title Data Type Format Access Points Subjects Studies Series Images License
Images CT, SEG DICOM
Download requires NBIA Data Retriever
32 100 936 81,548 CC BY 4.0
Lesion Notes XLS CC BY 3.0
Analysis Results Using This Collection
Related Datasets
Legend: Analysis Results| Collections

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

Zhao, B., Schwartz, L. H., Kris, M. G., & Riely, G. J. (2015). Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters (Version 3) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2015.u1x8a5nr

Related Publications

Publications by the Dataset Authors

The authors recommended this paper as the best source of additional information about this dataset:

  • 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

No publications by dataset authors were found.

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

Research Community Publications

TCIA maintains a list of publications which leverage our data.

  1. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.  DOI:10.18383/j.tom.2016.00235
  2. 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.

Other Publications Using this Data

TCIA maintains a list of publications which leverage our data.

  1. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.  DOI:10.18383/j.tom.2016.00235
  2. 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.

Previous Versions

Version 1: Updated 2012/10/18

Initial upload of data set.

Title Data Type Format Access Points Subjects Studies Series Images License

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 Studies Series Images 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