Summary

This collection contains clinical data and computed tomography (CT) from 137 head and neck squamous cell carcinoma (HNSCC) patients treated by radiotherapy. For these patients a pre-treatment CT scan was manual delineated by an experienced radiation oncologist of the 3D volume of the gross tumor volume. This dataset refers to the "H&N1" dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006). At time of previous publication, images of one subject had been unintentionally overlooked. In short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer.




Radiomics refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features. In the published analysis, 440 features quantifying tumor 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-tumor 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.

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 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 article addressing FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics.

Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: NSCLC-Radiomics, NSCLC-Radiomics-GenomicsNSCLC-Radiomics-Interobserver1RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (RIDER-LungCT-Seg).

For scientific or other inquiries about this dataset, please contact the TCIA Helpdesk.


Acknowledgements


We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • Leonard Wee, 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.
  • 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 & Harvard Medical School, Boston, Massachusetts, USA.


Data Access

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA No Commercial Limited Access License to help@cancerimagingarchive.net before accessing the data.

Data TypeDownload all or Query/FilterLicense

Images, Segmentations, and Radiation Therapy Structures (DICOM, 11.8 GB)

Clinical Data (CSV, zip)
Data Dictionary (txt)

Click the Versions tab for more info about data releases.

Detailed Description

Image Statistics


Modalities

CT, PT, RTSTRUCT, SEG

Number of Participants

137

Number of Studies

137

Number of Series

486

Number of Images

28918

Images Size (GB)28918

Citations & Data Usage Policy

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

Wee, L., & Dekker, A. (2019). Data from Head-Neck-Radiomics-HN1 [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.8kap372n.

Publication Citation

Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach, Nature Communications, Volume 5, Article Number 4006, June 03, 2014. DOI: http://doi.org/10.1038/ncomms5006. 

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Questions may be directed to help@cancerimagingarchive.net

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 3 (Current): 2020/07/29

Data TypeDownload all or Query/Filter
Images (DICOM, 11.8 GB)
Clinical Data (CSV, zip)
Data Dictionary (txt)

Added the chemotherapy schedule to the clinical data; one extra column added which is “chemotherapy_given”.

Added data dictionary for clinical data.

Version 2 (Current): 2019/09/20

Data TypeDownload all or Query/Filter
Images (DICOM, 11.8 GB)
Clinical Data (CSV)

Added DICOM Segmentations

Version 1: 2019/07/25

Data TypeDownload all or Query/Filter
Images (DICOM, 11.2 GB)
Clinical Data (CSV)






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