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Summary

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Excerpt

This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications.


In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted.  We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

The dataset described here (Lung1) was used to build a prognostic radiomic signature. The Lung3 dataset used to investigate the association of radiomic imaging features with gene-expression profiles consisting of 89 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics-Genomics.

For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School (hugo_aerts@dfci.harvard.edu).

 

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Localtab Group
Localtab
activetrue
titleData Access

Data Access

Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option.

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

Click the Versions tab for more info about data releases.

Localtab
titleDetailed Description

Detailed Description

Collection Statistics

 

Modalities

CT, RTSTRUCT

Number of Patients

422

Number of Studies

422

Number of Series

740

Number of Images

51,513

Image Size (GB)25

Radiation Oncologist Tumor Segmentations

The RTSTRUCT files in this data contain a manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume.  For viewing quickly we recommend Dicompyler (http://www.dicompyler.com/) which is an open source, cross-platform DICOM RT viewer.  Slicer has a SlicerRT module (http://slicerrt.github.io/index.html) which enables use of this kind of data.  The Radiotherapy DICOM toolkit may also be useful for working with this data (https://github.com/dicom/rtkit).

 

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.

Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

This collection may not be used for commercial purposes. This collection is freely available to browse, download, and use for scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Info
titleData Citation

Aerts, Hugo J. W. L., Rios Velazquez, Emmanuel, Leijenaar, Ralph T. H., Parmar, Chintan, Grossmann, Patrick, Carvalho, Sara, … Lambin, Philippe. (2015). Data From NSCLC-Radiomics. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.PF0M9REI

Info
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. http://doi.org/10.1038/ncomms5006  (link)

Info
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)

 

Other Publications Using This Data

TCIA maintains a list of publications which leverage our data. At this time we are not aware of any additional publications based on this data. If you have a publication you'd like to add please contact the TCIA Helpdesk.

Localtab
titleVersions

Version 2 (Current): Updated 2016/05/31

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

 Added 318 RSTRUCT files for existing subject imaging data

Version 1: Updated 2014/07/02

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