- Created by Justin Kirby, last modified by Paul Commean on Dec 16, 2015
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Summary
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). When using this data in scientific publications or technical reports please cite the following reference:
- Aerts, H. J. W. L. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5:4006 doi: 10.1038/ncomms5006 (2014). (link)
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.
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Detailed Description
Collection Statistics | 07/02/2014 |
---|---|
Modalities | CT |
Number of Patients | 422 |
Number of Studies | 422 |
Number of Series | 422 |
Number of Images | 51,195 |
Image Size (GB) | 25 |
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.
Restricted from Commercial Use
The data are not permitted for commercial applications. Please contact the associated data submitters with any questions about utilizing this data.
Citations & Data Usage Policy
This collection is freely available to browse, download, and use for commercial, 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:
NSCLC-Radiomics Citation
The Cancer Imaging Archive Team. Data From BREAST-DIAGNOSIS. doi:10.7937/K9/TCIA.2015.SDNRQXXR
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. 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)
Decoding Tumour Phenotype Citation
Aerts, H. J. W. L. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5:4006 doi: 10.1038/ncomms5006 (2014). (link)
Other Publications Using This Data
TCIA maintains a list of publications which leverage our data. At this time we are not aware of any publications based on this data. If you have a publication you'd like to add please contact the TCIA Helpdesk.
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