Training set of clinical meta-data for Local Control Challenge

This MD Anderson Cancer Center set of anonymized high-quality computed tomography (CT) scans with contrast represent a comparatively homogeneous, uniform cohort of 288 oropharynx cancer patients with detailed clinical history, consistent follow-up of > 2 years, known etiological/biological correlates (specifically, human papilloma virus status). Our major target is to assess/validate the radiomics workflow and predictive capacity of radiomics signatures from challenge participants.We imported the CT scans from the patients’ electronic medical records, that were performed before the initiation of the radiation treatment course. All the patients were treated using the IMRT modality. Some patients were simultaneously prescribed chemotherapy. We intended that the CT films would be as much representative of the original simulation CT scans that were used for treatment planning, in which no contrast was injected according to our institutional policy.Specifically, we posted around one-half of the CT scans from the dataset (138 patients), in DICOM-RT format, on the Kaggle in Class server system, as a “training set”. DICOM-RT files were fully anonymized, with expert physician segmenting primary tumor and lymph node as regions of interest, to eliminate segmentation-related uncertainty for challengers. The primary oropharyngeal tumor was segmented in red. Whereas, the metastatic cervical lymph nodes were segmented individually, rather than on the basis of the nodal level classification system. Both training and test sets include the following data for each DICOM-RT case:agegenderracetumor side and subsiteT-categoryN-categoryAJCC stagePathologic gradesmoking status (in pack-years)Challenge participants will also be able to download a “test" dataset, which includes the remaining randomly selected 150 patients' DICOM files and relevant clinical meta-data, with local control status blinded.Challenge participants will also be able to download a “test" dataset, which includes the remaining randomly selected half of the dataset, with local control status blinded.Challenge participants will also be able to download a “test" dataset, with the remaining random selected half of the dataset, which will have the local recurrence status blinded.本数据集源自MD安德森癌症中心（MD Anderson Cancer Center），包含经匿名化处理的高质量增强计算机断层扫描（CT，computed tomography）影像。该数据集对应288例口咽癌患者组成的均质性队列，患者临床病史详实，随访时长均超过2年，且明确携带病因学/生物学关联特征（具体为人乳头瘤病毒（HPV）感染状态）。本次数据集的核心目标为评估、验证挑战赛参赛者提出的放射组学工作流程，以及放射组学特征的预测性能。我们从患者电子病历中导入了放疗疗程启动前采集的CT影像。所有患者均采用调强放射治疗（IMRT，intensity-modulated radiation therapy），部分患者同步接受化疗。根据本机构的政策规定，用于治疗规划的原始模拟CT影像无需注射对比剂，因此本次提供的CT影像应尽可能匹配该类原始模拟CT影像。具体而言，我们将数据集中约半数的CT影像（对应138例患者）以DICOM-RT格式上传至Kaggle in Class服务器系统，作为训练集（training set）。所有DICOM-RT文件均已完成全流程匿名化处理，并由专业医师手动勾画出原发肿瘤与淋巴结作为感兴趣区域（ROI），以消除参赛者面临的分割不确定性。其中，口咽部原发肿瘤以红色标注；转移性宫颈淋巴结则采用单独勾画的方式，而非基于淋巴结分区分类系统进行标注。训练集与测试集（test set）均为每个DICOM-RT病例提供以下数据：年龄、性别、种族、肿瘤侧别与亚部位、T分期、N分期、AJCC分期、病理分级、吸烟史（以包-年为单位）。挑战赛参赛者可下载测试集（test set），该数据集包含剩余随机选取的150例患者的DICOM文件与相关临床元数据，所有病例的局部控制状态与局部复发状态均处于盲态。