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Localtab Group

titleData Access

Data Access

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter
Images, Segmentations, and Radiation Therapy Structures (DICOM, 33GB)


(Requires the NBIA Data Retriever.)

Lung1 clinical (CSV)

Click the Versions tab for more info about data releases.

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:

titleDetailed Description

Detailed Description

Collection Statistics



Number of Participants


Number of Studies


Number of Series


Number of Images


Image Size (GB)33

Radiation Oncologist Tumor Segmentations

The DICOM Radiotherapy Structure Sets (RTSTRUCT) and DICOM Segmentation (SEG) files in this data contain a manual delineation by a radiation oncologist of the 3D volume of the primary gross tumor volume ("GTV-1") and selected anatomical structures (i.e., lung, heart and esophagus). Of note, DICOM SEG objects contain a subset of annotations available in RTSTRUCT.

For viewing the annotations the authors recommend 3D Slicer that can be used to view both RTSTRUCT and SEG annotations (make sure you install the SlicerRT and QuantitativeReporting extensions first!).
Visualization of the DICOM annotations is also supported by the OHIF Viewer.

Other tools include:

  • Dicompyler is an open source, cross-platform DICOM RT viewer.
  • The Radiotherapy DICOM toolkit, which may also be useful for working with this data. 
  • dcmqi and Plastimatch libraries can be used to support conversion of the DICOM SEG and RTSTRUCT representations, respectively, into the popular research formats, such as NIfTI or NRRD.

Clinical Data

Corresponding clinical data can be found here: Lung1.clinical.csv.

Please note that survival time is measured in days from start of treatment. DICOM patients names are identical in TCIA and clinical data file.

titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Users of this data must abide by the Creative Commons Attribution-NonCommercial 3.0 Unported License under which it has been published. Attribution should include references to the following citations:

titleData Citation

Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., … Lambin, P. (2019). Data From NSCLC-Radiomics [Data set]. The Cancer Imaging Archive.

titlePublication Citation

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Cavalho, S., … Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group.  (link)

titleTCIA 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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)

Questions may be directed to

Other Publications Using This Data

TCIA maintains a list of publications that leverage our data. If you have a publication you'd like to add, please contact the TCIA Helpdesk.


Version 4 (Current): Updated 2020/10/20

Data TypeDownload all or Query/Filter
Images (DICOM, 33 GB)


(Requires the NBIA Data Retriever.)

Lung1 clinical (CSV)

  • RTSTRUCT and SEG study instance UID changed to match study instance uid with associated CT image.
  • Added missing structures in SEG files to match associated RTSTRUCTs.
  • Patient Id copied to Patient Name in CT images (for consistency).
  • Added 1 missing image for LUNG1-246.

Version 3: Updated 2019/10/23

Data TypeDownload all or Query/Filter
Images (DICOM, 29GB)


(Requires the NBIA Data Retriever.)

Lung1 clinical (CSV)

  • Re-checked and updated the RTSTRUCT files to amend issues in the previous submission due to missing RTSTRUCTS or regions of interest that were not vertically aligned with the patient image.
  • In 4 cases (LUNG1-083,LUNG1-095,LUNG1-137,LUNG1-246) re-submitted the correct CT images.
  • The regions of interest now include the primary lung tumor labelled as “GTV-1”, as well as organs at risk.
  • For one case (LUNG1-128) the subject does not have GTV-1 because it was actually a post-operative case; we retained the CT scan here for completeness.
  • Added DICOM SEGMENTATION objects to the collection, which makes it easier to search and retrieve the GTV-1 binary mask for re-use in quantitative imaging research.
  • Clinical data updated as follow-up time has been extended.

Version 2: Updated 2016/05/31 (version 2 removed as RTSTRUCTs or regions of interest were not vertically aligned with patient images. See version 3 for updated files).

Data TypeDownload all or Query/Filter
Images (DICOM, 25GB)

 not available, see version 3

Lung1 clinical (CSV)

 Added 318 RTSTRUCT files for existing subject imaging data

Version 1: Updated 2014/07/02

Data TypeDownload all or Query/Filter
Images (DICOM, 25GB)
Lung1 clinical (CSV)