CCDI-MCI: DICOM converted whole slide hematoxylin and eosin stained images from the Molecular Characterization Initiative of the National Cancer Institute's Childhood Cancer Data Initiative

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: CCDI-MCI. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below. Collection description The Molecular Characterization Initiative (MCI) [2] is a component of the National Cancer Institute’s (NCI) Childhood Cancer Data Initiative (CCDI). It offers state-of-the-art molecular testing at no cost to newly diagnosed children, adolescents, and young adults (AYAs) with central nervous system (CNS) tumors, soft tissue sarcomas (STS), certain rare childhood cancers (RAR), and certain neuroblastomas (NBL) treated at a Children’s Oncology Group (COG)–affiliated hospital. The goal of MCI is to enhance the understanding of genetic factors in pediatric cancers and to provide timely, clinically relevant findings to doctors and families to aid in treatment decisions and determine eligibility for certain planned COG clinical trials. The original images in vendor-specific format were collected on IRB-approved clinical trials or tissue banking studies from Children’s Oncology Group (COG) patients enrolled in EveryChild APEC14B1 protocol. Those images, augmented with the metadata describing their content, were provided to the IDC team for the purposes of archival, and were converted into DICOM Whole Slide Microscopy (SM) representation [3,4] using custom open source scripts and tools as described in [5]. The initial batch of the converted images was initially released in IDC in the CCDI-MCI collection with the IDC data release v19, with new cases/slides added in the subsequent versions. To learn how to access related clinical and genomic data accompanying this collection please see the CCDI-MCI page and CCDI Hub. Instructions on correlating this information with the images in this collection are available in this forum post. Files included A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced. ccdi_mci-idc_v21-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets ccdi_mci-idc_v21-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets ccdi_mci-idc_v21-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids) Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP. Download instructions Each of the manifests include instructions in the header on how to download the included files. To download the files using .s5cmd manifests: install idc-index package: pip install --upgrade idc-index download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd To download the files using .dcf manifest, see manifest header. Acknowledgments Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l. References [1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023). [2] https://www.cancer.gov/research/areas/childhood/childhood-cancer-data-initiative/programs/molecular-characterization [3] National Electrical Manufacturers Association (NEMA). DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD. at <https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8> [4] Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018). [5] Clunie, D., Fedorov, A. & Herrmann, M. D. ImagingDataCommons/idc-wsi-conversion: Initial release. (Zenodo, 2023). doi:10.5281/ZENODO.8240154本数据集源自美国国家癌症研究所（National Cancer Institute, 简称NCI）影像数据公共库（Imaging Data Commons, IDC）[1]，包含该库收录的图像及图像衍生数据。本数据集已转换为医学数字成像和通信（Digital Imaging and Communications in Medicine, DICOM）格式，并由IDC团队完成入库。您可通过IDC门户的CCDI-MCI页面浏览并可视化对应图像，亦可按照下文的下载说明，使用本Zenodo记录中附带的清单文件（manifest）下载该数据集的全部内容。 ## 数据集说明 分子表征倡议（Molecular Characterization Initiative, MCI）[2] 是美国国家癌症研究所（NCI）儿童癌症数据倡议（Childhood Cancer Data Initiative, CCDI）的组成部分。该倡议为在儿童肿瘤协作组（Children’s Oncology Group, COG）附属医院接受治疗的新发中枢神经系统（central nervous system, CNS）肿瘤、软组织肉瘤（soft tissue sarcoma, STS）、特定罕见儿童癌症（rare childhood cancer, RAR）及特定神经母细胞瘤（neuroblastoma, NBL）患者，包括儿童、青少年与年轻成人（adolescents and young adults, AYAs），提供免费的尖端分子检测服务。MCI的目标在于加深对儿童癌症遗传致病因素的认知，并向临床医师与患者家属提供及时且具有临床参考价值的检测结果，以辅助治疗决策，并评估患者参与COG后续计划临床试验的资格。 本数据集的原始图像采用厂商专属格式采集，源自纳入EveryChild APEC14B1研究方案的COG患者的伦理审查委员会（Institutional Review Board, IRB）核准临床试验或组织库研究。这些图像辅以描述其内容的元数据后，被提交至IDC团队用于归档，并依据文献[5]所述的自定义开源脚本与工具，转换为DICOM全视野显微图像（Whole Slide Microscopy, SM）格式[3,4]。首批转换后的图像以CCDI-MCI数据集的形式随IDC v19数据版本首次发布于IDC平台，后续版本陆续新增了病例与切片数据。 如需获取本数据集配套的临床与基因组数据的访问方式，请参阅CCDI-MCI页面及CCDI Hub平台。关于如何将此类信息与本数据集图像进行关联的说明，请参见本论坛帖子。 ## 包含文件 清单文件（manifest）的文件名可指示该数据集版本首次发布时对应的IDC数据版本。例如，`collection_id-idc_v8-aws.s5cmd` 对应IDC v8数据版本首次发布的`collection_id`数据集内容。若本Zenodo记录存在更新版本，则会标注对应数据集更新版本的发布时间。 ccdi_mci-idc_v21-aws.s5cmd：用于从IDC公共亚马逊云服务（Amazon Web Services, AWS）存储桶下载文件的清单文件 ccdi_mci-idc_v21-gcs.s5cmd：用于从IDC公共谷歌云存储（Google Cloud Storage, GCS）存储桶下载文件的清单文件 ccdi_mci-idc_v21-dcf.dcf：Gen3格式清单文件（详情请参阅 https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids） 请注意，以`-aws.s5cmd`结尾的清单文件对应存储于AWS存储桶中的文件，以`-gcs.s5cmd`结尾的清单文件对应存储于谷歌云存储中的文件。两类清单指向的实际文件完全一致，且在AWS与谷歌云平台间完成了镜像同步。 ## 下载说明 每份清单文件的文件头均包含对应包含文件的下载说明。 ### 使用.s5cmd格式清单文件下载文件 1. 安装idc-index工具包：执行命令 `pip install --upgrade idc-index` 2. 通过指定.s5cmd格式清单文件，下载本数据集中清单指向的文件：执行命令 `idc download manifest.s5cmd` 若使用.dcf格式清单文件下载文件，请参阅清单文件头中的说明。 ## 致谢 影像数据公共库团队的研究经费全部或部分来源于美国国立卫生研究院（National Institutes of Health, NIH）下属国家癌症研究所的联邦拨款，相关任务订单编号为HHSN26110071，合同编号为HHSN261201500003l。 ## 参考文献 [1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. 美国国家癌症研究所影像数据公共库：实现影像人工智能研究的透明化、可重复性与可扩展性. 《放射学》（Radiographics）, 43, (2023). [2] https://www.cancer.gov/research/areas/childhood/childhood-cancer-data-initiative/programs/molecular-characterization [3] 美国电气制造商协会（National Electrical Manufacturers Association, NEMA）. DICOM PS3.3 — 信息对象定义：A.32.8 VL全视野显微图像信息对象定义. 载于 <https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8> [4] Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. 数字病理学DICOM标准的实施. 《病理学信息学杂志》（Journal of Pathology Informatics）, 9, 37 (2018). [5] Clunie, D., Fedorov, A. & Herrmann, M. D. ImagingDataCommons/idc-wsi-conversion: Initial release. (Zenodo, 2023). doi:10.5281/ZENODO.8240154