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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.
Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: NSCLC-Radiomics, NSCLC-Radiomics-Genomics, NSCLC-Radiomics-Interobserver1, Head-Neck-Radiomics-HN1.
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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