Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that ‘radiomics’, or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual meeting.These public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set.
展开▼
机译:由于口咽癌目前在国内流行,因此在过去几年中,对口咽癌的研究越来越多。这些肿瘤用确定的(化学)放疗治疗,并具有局部复发作为临床失败的主要方式。最新数据表明,“放射学”或图像纹理分析的提取以从医学图像生成可挖掘的定量数据,可以反映各种癌症的表型。几组研究表明,在头颈癌中发展的放射学特征可能与生存结果相关。该数据描述符定义了头颈部放射性挑战的存储库,并与MICCAI学会2016年年会合作通过Kaggle in Class平台执行,这些公共挑战旨在利用放射性组和/或机器学习工作流程来区分HPV表型。一项挑战(HPV状态挑战),并在分段,临床策划的匿名口咽癌(OPC)数据集中识别出第二个挑战(原发性复发预测挑战)中原发性肿瘤体积局部复发的患者。
展开▼