HEAD-NECK-RADIOMICS-HN1

This collection contains images from 137 head and neck cancer patients. For these patients pre-treatment CT (some including co-registered PET) scans and manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume. Clinical outcome data are available for 135 of these subjects. This dataset refers to the Head and Neck1 dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006). In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with either lung or head-and-neck cancer. Radiomics refers to the comprehensive quantication 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 identied as signicant 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 identies a general prognostic phenotype existing in both lung and head-andneck 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 intended to be open access to support repeatability and reproducibility of research in the radiomics domain. We additionally expect this dataset to be used as a reference set in the search for image-based biomarkers that may be prognostic for overall survival and head-and-neck cancer. This dataset will be the subject of an upcoming Nature Data article addressing OPEN, FACT and FAIR radiomics practices to support transparency, harmonization and collaboration in the study of radiomics.

Acknowledgements

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

• Leonard Wee, Maastro Clinic, Maastricht, Limburg (Netherlands) and Hugo Aerts, Dana-Farber Cancer Institute/Harvard Medical School, Boston, Mass