Summary
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This collection contains images clinical data and computed tomography (CT) from 22 non-small cell lung cancer (NSCLC) radiotherapy patients. For each 21 of these patients with pre-treatment hybrid PET- CT scans, repeated blinded manual delineations by 5 five different radiation oncologist oncologists of the 3D volume of the gross tumor volume on CT and clinical outcome data are available. The above was repeated with the same set of 5 five radiation oncologists, using an in-house autosegmentation tool for initial delineation followed by manual adjustment of the primary gross tumor volume outline. The delineations were cloned to coregistered PET and resampled in the grid spacing of the PET imageFor one patient, clinical data and CT was available but the tumor delineations were not extracted. This patient was included in this collection for the sake of completeness. This dataset refers to the Interobserver "Multiple delineation" dataset of the study published in Nature Communications (httphttps://doi.org/10.1038/ncomms5006). In short, the publicates publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-andneck 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 geneexpression gene-expression patterns. These data suggest that radiomics identities 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. The delineations are provided in two formats; DICOM RTSTRUCT contains slice by slice contour points of the external outline of the primary tumour. DICOM SEGMENTATION contains binary masks of the same primary tumour. The nomenclature of the structures are as follows:
Side note : Radiation oncologists denoted “1” and “3” were trainee radiation oncologists at the time of this experiment. Radiation oncologists “2”, “4” and “5” were extensively experienced at the time of this experiment. This dataset is intended to be open access to support repeatability and reproducibility of research in the radiomics domain. This dataset will be the subject of an upcoming Nature Data article addressing OPEN, FACT and has been referenced in Medical Physics Dataset Article addressing FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics (https://doi.org/10.1002/mp.14322). |
Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: HEAD-NECK-RADIOMICS-HN1 , NSCLC-Radiomics-Interobserver1, NSCLC-Radiomics-Genomics, RIDER-LungCT-Seg.
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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.
- Dirk de Ruysscher, 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.
- Petros Kalendralis, MAASTRO (Dept of Radiotherapy), Maastricht University Medical Centre+, Maastricht, Limburg, The Netherlands.
- Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 from NCIHospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
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