Annotations for Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma/PNET

This dataset contains image annotations derived from the NCI Clinical Trial "<a href="https://doi.org/10.7937/TCIA.582B-XZ89">Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma/PNET (ACNS0332)</a>".  This curated dataset provides a comprehensive picture of imaging in pediatric patients with newly diagnosed primitive neuroectodermal tumors throughout their treatment and until any potential relapse. This is the largest known dataset of patients with supratentorial primitive neuroectodermal tumors and pineoblastomas. This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.<h3>Annotation Protocol</h3>For each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following time points were annotated:<ol><li>Pre-surgical study</li><li>Post-surgical study [if applicable]</li><li>Follow-up study at the completion of radiotherapy.</li><li>Follow-up study at the end of chemotherapy.</li><li>Follow-up study relapse [if applicable]</li></ol>At each time point, 3D segmentations (DICOM SEG), seed points (DICOM RTSTRUCT) and negative finding assessments (DICOM RTSTRUCT) were created:<ol><li>Enhancing tumor on an axial 3D T1 post contrast sequence<ol><li>If not available, a 3D post contrast sequence in another plane was used.</li><li>If no 3D post contrast sequence was available, the tumor was annotated in all 3 planes utilizing 2D post contrast sequences.</li><li>On post-contrast sequences, the entire tumor, including the cystic and non enhancing components was annotated.</li><li>Any resection cavity or post-op changes/products was excluded.</li></ol></li><li>Edema on an axial T2 FLAIR sequence<ol><li>If not available, an axial T2 or other T2 weighted sequence was used.</li><li>The segmentation mask contains both the edematous tissue and the tumor.</li></ol></li><li>The portion of the tumor demonstrating restricted diffusion on an ADC sequence</li><li>Up to 5 metastatic lesions within the brain and and up to 5 metastatic lesions in the spine as demonstrated on whatever T1 post contrast sequence they are visualized on<ol><li>When present, the 5 largest lesions were annotated.</li></ol></li><li>A manually placed seed point (kernel) were created for each segmented structure<ol><li>The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.</li><li>Spinal metastases, which are too small to apply a volumetric mask to, only have a seed point annotation.</li></ol></li><li>If no seed points or segmentations were generated a "Negative Assessment Report" RTSTRUCT file was created to record this fact.</li><li>To ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.</li></ol><h3>Important supplementary information and sample code</h3><ol><li>A spreadsheet containing a variety of useful metadata about the annotations, including calculated tumor volumes, is available in the <strong>Data Access</strong> section below.</li><li>Important information about how to interpret the DICOM annotation data can be found on the <strong>Detailed Description</strong> section below.  It includes information about specific tags which document where the tumor was found, whether it was enhancing/non-enhancing, which study time point the annotation relates to, details for lesion tracking across time points, etc.</li><li>A Jupyter notebook demonstrating how to use the <a href="/display/NBIA/NBIA+Data+Retriever+Command-Line+Interface+Guide">NBIA Data Retriever Command-Line Interface</a> application and our <a href="/display/Public/NBIA+Search+with+Authentication+REST+API+Guide">REST API (with authentication)</a> to access these data can be found in the <strong>Additional Resources</strong> section below.</li><li>Instructions for visualizing these data in 3D Slicer can be found in the <strong>Additional Resources</strong> section below.</li></ol>

本数据集收录了源自美国国家癌症研究所临床试验（<a href="https://doi.org/10.7937/TCIA.582B-XZ89">化疗与放射治疗在治疗新诊断、未经治疗的、高风险髓母细胞瘤/PNET（ACNS0332）年轻患者的应用</a>）的图像标注。该精选数据集全面展示了儿童患者在接受新诊断的原位神经外胚层肿瘤治疗期间及其潜在复发前的影像学表现。这是已知关于顶叶原位神经外胚层肿瘤和松果体瘤患者规模最大的数据集。该数据集的生成是作为国家癌症研究所项目的一部分，旨在通过添加标注以增强TCIA数据集的价值，从而服务于癌症研究者和人工智能开发者。<h3>标注协议</h3>针对每位患者，均对所有扫描进行了审查，以识别并标注临床相关的时点和序列/系列。初期的标注由持有MBBS学位或更高学位的国际放射学专家团队完成，随后由美国认证的放射学专家进行复核，以确保标注的准确性。在典型患者中，以下时点被标注：<ol><li>术前研究</li><li>术后研究[如适用]</li><li>放疗完成后的随访研究</li><li>化疗完成后的随访研究</li><li>复发后的随访研究[如适用]</li></ol>在每个时点，均创建了3D分割（DICOM SEG）、种子点（DICOM RTSTRUCT）和阴性发现评估（DICOM RTSTRUCT）：<ol><li>在轴位3D T1增强序列上增强的肿瘤<ol><li>如不可用，则使用另一个平面的3D增强序列。</li><li>如无3D增强序列可用，则利用2D增强序列在所有三个平面上标注肿瘤。</li><li>在增强序列上，标注包括囊性和非增强成分的整个肿瘤。</li><li>排除任何切除空腔或术后变化/产物。</li></ol></li><li>在轴位T2 FLAIR序列上的水肿<ol><li>如不可用，则使用轴位T2或其他T2加权序列。</li><li>分割掩码包含水肿组织和肿瘤。</li></ol></li><li>在ADC序列上表现出受限扩散的肿瘤部分</li><li>在脑部和脊柱上最多5个转移性病变，这些病变在可视化的T1增强序列上被展示出来<ol><li>存在时，标注了5个最大的病变。</li></ol></li><li>为每个分割结构创建了一个手动放置的种子点（核）<ol><li>每个分割的种子点提供在单独的DICOM RTSTRUCT文件中。</li><li>对于太小而无法应用体积掩模的脊柱转移，仅进行种子点标注。</li></ol></li><li>如果没有生成种子点或分割，则创建了一个“阴性评估报告”RTSTRUCT文件以记录此事实。</li><li>为确保高标准的准确性和数据质量，每位标注均由另一位读者进行了复核。</li></ol><h3>重要补充信息和示例代码</h3><ol><li>包含关于标注的各种有用元数据的电子表格，包括计算的肿瘤体积，可在下面的<strong>数据访问</strong>部分找到。</li><li>有关如何解释DICOM标注数据的重要信息可在下面的<strong>详细描述</strong>部分找到。它包括有关特定标签的信息，这些标签记录了肿瘤的位置、是否增强/非增强、标注与哪些研究时间点相关、病变随时间点变化的详细信息等。</li><li>一个Jupyter笔记本演示了如何使用<a href="/display/NBIA/NBIA+Data+Retriever+Command-Line+Interface+Guide">NBIA数据检索命令行界面</a>应用程序和我们的<a href="/display/Public/NBIA+Search+with+Authentication+REST+API+Guide">REST API（带认证）</a>来访问这些数据，可在下面的<strong>其他资源</strong>部分找到。</li><li>在下面的<strong>其他资源</strong>部分提供了在3D Slicer中可视化这些数据的说明。</li></ol>