CMB-GEC: DICOM converted Slide Microscopy images for the Cancer Moonshot Biobank initiative Gastroesophageal Cancer collection

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. 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 Cancer Moonshot Biobank (CMB) is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites. CMB program is organized into multiple cancer-specific collections. Digital pathology images for each of those collections were converted into DICOM representation by the IDC team and are shared via IDC. This entry corresponds to the CMB-GEC collection (Gastroesophageal Cancer). Digital pathology images, augmented with the metadata describing their content, were converted into DICOM Whole Slide Microscopy (SM) representation [2,3] using custom open source scripts and tools as described in [4]. 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. For each of the collections, the following manifest files are provided: <collection_id>-idc_v20-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets <collection_id>-idc_v20-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets <collection_id>-idc_v20-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] 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> [3] 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). [4] Clunie, D., Fedorov, A. & Herrmann, M. D. ImagingDataCommons/idc-wsi-conversion: Initial release. (Zenodo, 2023). doi:10.5281/ZENODO.8240154本数据集对应美国国家癌症研究所（National Cancer Institute, NCI）影像数据公共库（Imaging Data Commons, IDC）[1] 提供的图像集合及/或图像衍生数据。本数据集已被IDC团队转换为医学数字成像与通信（DICOM）格式并入库。您可通过IDC门户访问此处的链接浏览并可视化对应图像。您可借助本Zenodo记录中包含的清单文件，按照下文的下载说明下载该集合的全部内容。 ## 数据集集合说明 美国国家癌症研究所癌症登月计划生物样本库（Cancer Moonshot Biobank, CMB）是美国国家癌症研究所发起的资助项目，旨在支持当前及未来针对药物耐药性、敏感性及其他NCI赞助的癌症研究计划，以提升研究者对癌症的认知，以及干预癌症发生与进展的手段。在本研究过程中，研究团队将从至少10种癌症类型的1000余名患者中纵向收集生物样本（医疗过程中获取的血液与组织样本）及关联数据；这些患者涵盖美国人口统计多样性特征，且均在多个NCI社区肿瘤学研究计划（NCI Community Oncology Research Program, NCORP）试点机构接受标准癌症治疗。 CMB项目下设多个癌症特异性数据集集合。IDC团队已将各集合的数字病理图像转换为DICOM格式，并通过IDC平台共享。本条目对应CMB-GEC集合（胃食管癌（Gastroesophageal Cancer））。 数字病理图像及其内容描述元数据已通过自定义开源脚本与工具，按照文献[4]所述流程转换为DICOM全视野数字化显微图像（Whole Slide Microscopy, SM）格式[2,3]。 ## 包含文件 清单文件名会标注该集合版本首次引入的IDC数据发布版本。例如，`collection_id-idc_v8-aws.s5cmd`对应首次在IDC数据发布版本v8中推出的`collection_id`集合内容。若本Zenodo页面存在后续版本，将标注对应集合后续版本的推出时间。 针对每个数据集集合，提供以下清单文件： 1. `<collection_id>-idc_v20-aws.s5cmd`：可从IDC公共亚马逊云服务（Amazon Web Services, AWS）存储桶下载的文件清单 2. `<collection_id>-idc_v20-gcs.s5cmd`：可从IDC公共谷歌云存储（Google Cloud Storage, GCS）存储桶下载的文件清单 3. `<collection_id>-idc_v20-dcf.dcf`：Gen3格式清单（详情请见<https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids>） 注意：以`-aws.s5cmd`结尾的清单文件引用存储于AWS存储桶中的文件，而`-gcs.s5cmd`结尾的清单文件引用存储于谷歌云存储中的文件。实际文件内容完全一致，且在AWS与谷歌云平台（Google Cloud Platform, GCP）间实现镜像同步。 ## 下载说明 每份清单文件的头部均包含对应包含文件的下载说明。 ### 使用.s5cmd格式清单下载文件 1. 安装`idc-index`工具包：`pip install --upgrade idc-index` 2. 通过传入.s5cmd格式清单文件，下载本数据集清单中引用的全部文件：`idc download manifest.s5cmd` ### 使用.dcf格式清单下载文件 请参照清单文件头部的说明操作。 ## 致谢 影像数据公共库团队的研究工作全部或部分获得了美国国家癌症研究所、美国国立卫生研究院的联邦资金资助，项目任务订单编号为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：实现影像人工智能领域的透明度、可重复性与可扩展性. *Radiographics*, 43, (2023). [2] 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全视野数字化显微图像IOD. 见于<https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8> [3] 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. 数字病理学领域DICOM标准的实施. *Journal of Pathology Informatics*, 9, 37 (2018). [4] Clunie, D., Fedorov, A. & Herrmann, M. D. ImagingDataCommons/idc-wsi-conversion: 初始发布版. (Zenodo, 2023). doi:10.5281/ZENODO.8240154