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This dataset refers to the RIDER dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006). In short, this publication used the dataset to select for repeatable radiomics features in a test-retest context. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In the published 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.
Note
(RIDER-2283289298) only has segmentations associated with the retest.
(RIDER-5195703382) only has segmentations associated with the test.
(RIDER-8509201188) only has segmentations associated with the test.
(RIDER-9762593735) not included in the data set due to missing delineations
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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 |
Download
Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. This analysis set 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 help@cancerimagingarchive.net.
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Data Type | Download all or Query/Filter |
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| RIDER Lung CT Images (DICOM, 912 Mbytes) | |
Segmentations ( |
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Note
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(RIDER-2283289298) only has segmentations associated with the retest.
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(RIDER-5195703382) only has segmentations associated with the test.
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(RIDER-8509201188) only has segmentations associated with the test.
DICOM, XXX Mbytes) |
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