Description
DOI
doi:10.7937/K9/TCIA.2014..ua0JGpDg
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This data applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer which are described in Nature Communications (
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Authors
Hugo J. W. L. Aerts; Emmanuel Rios Velazquez; Ralph T. H. Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M. Rietbergen; C. René Leemans; Andre Dekker; John Quackenbush; Robert J. Gillies; Philippe Lambin
TCIA Shared List
1038/ncomms5006). The various arms of the study are represented in TCIA as distinct Collections including NSCLC-Radiomics (Lung1), NSCLC-Radiomics-Genomics (Lung3), Head-Neck-Radiomics-HN1 (H&N1), NSCLC-Radiomics-Interobserver1 (Multiple delineation), and RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
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Citation
When using this data, please cite the original publication:
http://www.nature.com/ncomms/2014/140603/ncomms5006/full/ncomms5006.html
(RIDER-LungCT-Seg) (RIDER test/retest).
Description
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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.
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Download
Note: This data is restricted for commercial use. Please contact Hugo Aerts, hugo_aerts@dfci.harvard.edu with any questions on usage.
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