Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting

Many Cancers routinely identified by imaging haven’t yet benefited from recent advances in computer science. Approaches such as machine learning and deep learning can generate quantitative tumor 3D volumes, complex features and therapy-tracking temporal dynamics. However, cross-disciplinary researchers striving to develop new approaches often lack disease understanding or sufficient contacts within the medical community. Their research can greatly benefit from labeling and annotating basic information in the images such as tumor locations, which are obvious to radiologists.Crowd-sourcing the creation of publicly-accessible reference data sets could address this challenge. In 2011 the National Cancer Institute funded development of The Cancer Imaging Archive (TCIA), a free and open-access database of medical images. However, most of these collections lack the labeling and annotations needed by image processing researchers for progress in deep learning and radiomics. As a result, TCIA has partnered with the Radiological Society of North America (RSNA) and numerous academic centers to harness the vast knowledge of RSNA meeting attendees to generate these tumor markups.  Data sets annotated included CT scans from 352 subjects from the <a href="https://cancerimagingarchive.net/collection/tcga-luad/" target="_blank" rel="noopener">The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD)</a>, <a href="https://cancerimagingarchive.net/collection/tcga-kirc/" target="_blank" rel="noopener">The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC)</a>, <a href="https://cancerimagingarchive.net/collection/tcga-lihc/" target="_blank" rel="noopener">The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC)</a>, and <a href="https://cancerimagingarchive.net/collection/tcga-ov/" target="_blank" rel="noopener">The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV)</a> collections on TCIA.A full explanation of the project can be seen in the Detailed Description.

众多通过影像学常规诊断的癌症尚未从计算机科学领域的最新进展中获益。诸如机器学习和深度学习等手段能够生成肿瘤的三维体积、复杂特征以及治疗追踪的时间动态。然而，致力于开发新方法的跨学科研究人员往往缺乏对疾病的理解，或在医学界缺乏足够的联系。他们的研究若能从对图像中基本信息的标注和注释中获益良多，如肿瘤位置，这对于放射科医生而言一目了然。通过众包创建可供公众访问的参考数据集，可以解决这一挑战。2011年，美国国家癌症研究所资助了《癌症影像存档》（TCIA）的开发，这是一个免费且公开的医学影像数据库。然而，这些收藏中的大部分缺乏图像处理研究人员在进行深度学习和放射组学进展所必需的标注和注释。因此，TCIA与北美放射学会（RSNA）以及众多学术中心合作，利用RSNA会议与会者的丰富知识来生成这些肿瘤标记。标注的数据集包括来自352名受试者的CT扫描，这些受试者分别来自以下数据集：癌症基因组图谱肺腺癌集合（TCGA-LUAD）、癌症基因组图谱肾肾细胞癌集合（TCGA-KIRC）、癌症基因组图谱肝细胞癌集合（TCGA-LIHC）以及癌症基因组图谱卵巢癌集合（TCGA-OV），这些数据集均存储于TCIA。关于该项目的详细说明，请参阅详细描述。