论文标题：A radiogenomic dataset of non-small cell lung cancer
期刊：
作者：Shaimaa Bakr, Olivier Gevaert, Sebastian Echegaray, Kelsey Ayers, Mu Zhou, Majid Shafiq, Hong Zheng, Jalen Anthony Benson, Weiruo Zhang, Ann N. C. Leung, Michael Kadoch, Chuong D. Hoang, Joseph Shrager, Andrew Quon, Daniel L. Rubin, Sylvia K. Plevritis, Sandy Napel
发表时间：2018/10/16
数字识别码： 10.1038/sdata.2018.202
原文链接：
随着精准医疗的进步，癌症的明升手机版影像生物标志物有望用于改善患者的护理。与基因组生物标志物相比，影像学生物标志物具有以下优势：无创、可从整体上表征异质性肿瘤，而活检仅能获得有限的肿瘤组织。
最近发表在《明升体育app-数据》期刊的一篇文章A radiogenomic dataset of non-small cell lung cancer中，来自斯坦福大学明升手机版院的Sandy Napel及其团队基于211名患者的非小细胞肺癌(NSCLC)建立了一个独特的放射基因组数据集。该数据集包括计算机断层扫描数据(Computed Tomography-CT)、正电子发射断层扫描(PET)/CT图像数据、使用受控词汇表对明升手机版成像中观察到的肿瘤进行语义注释的数据，以及CT扫描中的肿瘤分割图。研究者们还将明升手机版成像数据与手术切除的肿瘤组织的基因突变分析结果、基因表达微阵列和RNA测序数据以及临床数据（包括患者存活数据）进行了结合。
该数据集的创建便于发现肿瘤分子和明升手机版影像特征之间的潜在关系，以及便于开发和评估预后的明升手机版影像生物标志物。
摘要：Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
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期刊介绍： () is a peer-reviewed, open-access journal for descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data. Scientific Data welcomes submissions from a broad range of research disciplines, including descriptions of big or small datasets, from major consortiums to single research groups. Scientific Data primarily publishes Data Descriptors, a new type of publication that focuses on helping others reuse data, and crediting those who share.
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（来源：明升手机版(明升官网)）
