{"id":5605,"date":"2023-09-04T03:11:58","date_gmt":"2023-09-04T03:11:58","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/head-neck-radiomics-hn1\/"},"modified":"2023-09-13T11:59:24","modified_gmt":"2023-09-13T11:59:24","slug":"head-neck-radiomics-hn1","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/head-neck-radiomics-hn1\/","title":{"rendered":"HEAD-NECK-RADIOMICS-HN1"},"featured_media":7878,"template":"","citation-tax":[],"cancer_types":["Head and Neck Cancer"],"citations":[4490,4491,2925],"collection_doi":"10.7937\/tcia.2019.8kap372n","collection_download_info":"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<\/a> to\u00a0help@cancerimagingarchive.net<\/a>\u00a0before accessing the data.\n\nClick the Versions tab for more info about data releases.","collection_downloads":[5058,5059,5060],"full_export":"

Summary<\/h2>

This collection contains clinical data and computed tomography (CT) from\u00a0137\u00a0head 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.\u00a0This dataset refers to the "H&N1" dataset of the study published in Nature Communications (http:\/\/doi.org\/10.1038\/ncomms5006<\/a>).\u00a0At time of previous publication, images of one subject had been unintentionally overlooked.\u00a0In short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer.<\/p>

<\/span>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.<\/p>

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.<\/p>

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.<\/p>

Other data sets in the Cancer Imaging Archive that were used in the same\u00a0<\/span><\/span>study published in Nature Communications<\/a>: NSCLC-Radiomics<\/a>, <\/span>\u00a0NSCLC-Radiomics-Genomics<\/a>,\u00a0<\/span><\/span>NSCLC-Radiomics-Interobserver1<\/a>,\u00a0RIDER-LungCT-Seg<\/a>.<\/p>

For scientific or other inquiries about this dataset, please\u00a0contact TCIA's Helpdesk<\/a>.<\/span><\/p>

Acknowledgements<\/strong><\/p>

We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p>