Summary
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This collection contains images from 89 non-small cell lung cancer (NSCLC) patients that were treated with surgery. For these patients pretreatment CT scans, gene expression, and clinical data are available. This dataset refers to the Lung3 dataset of the study published in Nature Communications |
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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. |
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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:
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title | Reference |
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Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: HEAD-NECK-RADIOMICS-HN1, NSCLC-Radiomics-Interobserver1, RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach |
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For scientific or other inquiries about this dataset, please contact TCIA's Helpdesk. |
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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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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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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:
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title | NSCLC-Radiomics Citation |
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title | Publication Citation |
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Version 1 (Current): Updated 2014/07/02 |
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Data Access
Imaging Data
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You can view and download these images on The Cancer Imaging Archive by clicking and selecting the NSCLC-Radiomics-Genomics collection. |
Collection Statistics | (updated 7/2/2014) |
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Modalities | CT |
Number of Patients | 89 |
Number of Studies | 89 |
Number of Series | 89 |
Number of Images | 13,482 |
Images Size (GB) | 6.6 |
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
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Gene-expression Data
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.
Clinical Data
Corresponding clinical data can be found here: Lung3.metadata.xls.
Restricted from Commercial Use
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