HEAD-NECK-RADIOMICS-HN1

<p>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 (<a href="http://doi.org/10.1038/ncomms5006">http://doi.org/10.1038/ncomms5006</a>). 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.</p><p>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><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><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><p>Other data sets in the Cancer Imaging Archive that were used in the same <a href="http://www.nature.com/ncomms/2014/140603/ncomms5006/full/ncomms5006.html">study published in Nature Communications</a>: <a href="https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI" target="_blank" rel="noopener">NSCLC-Radiomics</a>,  <a href="/collection/nsclc-radiomics-genomics/" target="_blank" rel="noopener">NSCLC-Radiomics-Genomics</a>, <a href="/collection/nsclc-radiomics-interobserver1/" target="_blank" rel="noopener">NSCLC-Radiomics-Interobserver1</a>, <a href="/analysis-result/rider-lungct-seg/" target="_blank" rel="noopener">RIDER-LungCT-Seg</a>.</p>

本集合囊括了137名头颈鳞状细胞癌（HNSCC）患者的临床数据及计算机断层扫描（CT）图像，患者均接受过放疗治疗。针对这些患者，经验丰富的放射肿瘤科医师对治疗前的CT扫描图像进行了人工描绘，以确定大体肿瘤体积的3D体积。本数据集对应于发表在《自然通讯》杂志上的研究中的“H&N1”数据集（http://doi.org/10.1038/ncomms5006）。在前期出版时，一位受试者的图像不慎被遗漏。简而言之，该出版物采用放射组学方法对1,019名患有肺癌或头颈癌患者的计算机断层扫描数据进行量化分析。放射组学指的是通过应用大量定量图像特征，对肿瘤表型进行全面量化。在已发表的统计分析中，提取了440个量化肿瘤图像强度、形状和纹理的特征。我们发现，大量放射组学特征在独立数据集中具有预后价值，其中许多特征在先前的研究中并未被认定为具有显著性。放射基因组学分析揭示了与潜在基因表达模式相关的预后放射组学特征，这些特征能够捕捉肿瘤内部的异质性。这些数据表明，放射组学识别出存在于肺癌和头颈癌中的普遍预后表型。这可能在临床实践中产生重要影响，因为影像学检查是常规使用的，它为以低成本改善癌症治疗决策支持提供了前所未有的机遇。本数据集作为开放获取资源提供，以支持放射组学研究结果的重复性和可再现性。本数据集将是即将发表的关于FAIR放射组学实践的文章的主题，旨在促进透明度、协调和放射组学领域的合作。自版本2（发布日期为2019年9月20日）起，我们包括了原发性肿瘤大体肿瘤体积的DICOM SEGMENTATION以及伴随DICOM轴位图像的DICOM RTSTRUCT文件。本数据集作为开放获取资源提供，以支持放射组学研究结果的重复性和可再现性。本数据集将是即将发表的关于FAIR放射组学实践的文章的主题，旨在促进透明度、协调和放射组学领域的合作。与发表在《自然通讯》杂志上的同一研究使用的数据集包括：NSCLC-Radiomics、NSCLC-Radiomics-Genomics、NSCLC-Radiomics-Interobserver1、RIDER-LungCT-Seg。