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 (Lung3) was used to investigate the association of radiomic imaging features with gene-expression profiles. The Lung2 dataset used for training the radiomic biomarker and consisting of 422 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics. Other datasets hosted on TCIA that are described in this study include: Head-Neck-Radiomics-HN1, NSCLC-Radiomics-Interobserver1, RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School (firstname.lastname@example.org).
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|Data Type||Download all or Query/Filter|
|Images (DICOM, 6.6GB)|
|Lung3 clinical (CSV)|
|Gene Expression (web)|
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Number of Patients
Number of Studies
Number of Series
Number of Images
|Image Size (GB)||6.6|
Corresponding microarray data acquired for the imaging samples are available at National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (Link to GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58661). The patient names used to identify the cases on GEO are identical to those used in the DICOM files on TCIA and in the clinical data spreadsheet.
Corresponding clinical data can be found here: Lung3.metadata.xls.
Please note that survival time is measured in days from start of treatment. DICOM patients names are identical in TCIA and clinical data file.
Citations & Data Usage Policy
This collection may not be used for commercial purposes. It is available to browse, download, as outlined in the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) https://creativecommons.org/licenses/by-nc/3.0/. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to email@example.com.
Please be sure to include the following citations in your work if you use this data set:
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-Genomics. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.L4FRET6Z
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)
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 that 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.
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