Summary
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scan was manual delineated by an experienced radiation oncologist of the 3D volume of the gross tumor volume.
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This dataset refers to the
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"H&N1" dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006).
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At time of previous publication, images of one subject had been unintentionally overlooked. In short,
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the publication
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used a
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radiomics approach to computed tomography data of 1,019 patients with
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lung or head-and-neck cancer.
Radiomics refers to the comprehensive
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quantification of
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tumor phenotypes by applying a large number of quantitative image features. In
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the published analysis, 440 features quantifying
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tumor image intensity, shape, and texture
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were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not
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identified as
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significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-
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tumor heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics
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identifies a general prognostic phenotype existing in both lung and head-and-
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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.
This dataset is
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provided as open access to support repeatability and reproducibility of research in
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radiomics
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.
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This dataset will be the subject of an upcoming article addressing FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics.
From version 2 (release date 09/20/2019) onwards we included the primary neoplasm gross tumour volume delineations in DICOM SEGMENTATION as well as DICOM RTSTRUCT files that accompanied the DICOM axial images. This dataset is provided as open access to support repeatability and reproducibility of research in radiomics. This dataset will be the subject of an upcoming
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article addressing
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FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics.
Other data sets in the
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Cancer Imaging Archive that were used in the same study published in Nature Communications: NSCLC-Radiomics, NSCLC-Radiomics-Genomics, NSCLC-Radiomics-Interobserver1, RIDER-LungCT-Seg.
For scientific or other inquiries about this dataset, please contact TCIA's Helpdesk.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Leonard Wee
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- , MAASTRO (Dept of Radiotherapy), Maastricht University Medical Centre+, Maastricht, Limburg, The Netherlands.
- Frank Hoebers, MAASTRO (Dept of Radiotherapy), Maastricht University Medical Centre+, Maastricht, Limburg, The Netherlands
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- .
- Andre Dekker, MAASTRO (Dept of Radiotherapy), Maastricht University Medical Centre+, Maastricht, Limburg, The Netherlands.
- Hugo Aerts, Computational Imaging and Bioinformatic Laboratory, Dana-Farber Cancer Institute
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- & Harvard Medical School, Boston,
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- Massachusetts, USA.
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These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
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