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Summary

This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications [ref].


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:

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

Imaging Data

You can view and download these images on The Cancer Imaging Archive by logging in to TCIA and selecting the NSCLC-Radiomics collection.

Collection Statistics

(updated 2/28/2013)

Modalities

CT

Number of Patients

422

Number of Studies

422

Number of Series

422

Number of Images

51,195

If you are unsure how to download this Collection please view our quick guide on Searching by Collection or refer to our The Cancer Imaging Archive User's Guide for more detailed instructions on using the site.

Shared Lists

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Clinical Data

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

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.

 

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