Versions Compared
Key
- This line was added.
- This line was removed.
- Formatting was changed.
Summary
This collection contains
images from 137 head and neck cancer patientsclinical 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
(some including co-registered PET) scans and manual delineation by ascan was manual delineated by an experienced radiation oncologist of the 3D volume of the gross tumor volume.
Clinical outcome data are available for 135 of these subjects. ThisThis dataset refers to the
Head and Neck1"H&N1" dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006)
. In. At time of previous publication, images of one subject had been unintentionally overlooked. In short,
thisthe publication
appliesused a
radiomicradiomics approach to computed tomography data of 1,019 patients with
either lung or head-and-neck cancer.
[picture]
Radiomics refers to the comprehensive
quantification of
tumourtumor phenotypes by applying a large number of quantitative image features. In
presentthe published analysis, 440 features quantifying
tumourtumor 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
identiedidentified as
signicantsignificant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-
tumourtumor heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics
identiesidentifies a general prognostic phenotype existing in both lung and head-and-
andneckneck 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
theradiomics
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 Dataarticle addressing
OPEN, FACT andFAIR radiomics practices to support transparency, harmonization and collaboration
in the study ofon radiomics.
For scientific inquiries about this dataset, please contact Dr Leonard Wee (leonard.wee@maastro.nl) and Prof Andre Dekker (andre.dekker@maastro.nl) at MAASTRO Clinic/Maastricht University Medical Centre+ and Maastricht University, The Netherlands.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Leonard Wee,
Maastro ClinicMAASTRO (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,
MassMassachusetts, USA.
Localtab Group | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|