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. 2021 Aug 15;81(16):4188-4193.
doi: 10.1158/0008-5472.CAN-21-0950. Epub 2021 Jun 15.

NCI Imaging Data Commons

Andrey Fedorov  1 , William J R Longabaugh  2 , David Pot  3 , David A Clunie  4 , Steve Pieper  5 , Hugo J W L Aerts  6   7   8 , André Homeyer  9 , Rob Lewis  10 , Afshin Akbarzadeh  11 , Dennis Bontempi  6 , William Clifford  2 , Markus D Herrmann  12 , Henning Höfener  9 , Igor Octaviano  10 , Chad Osborne  3 , Suzanne Paquette  2 , James Petts  13 , Davide Punzo  10 , Madelyn Reyes  3 , Daniela P Schacherer  9 , Mi Tian  2 , George White  2 , Erik Ziegler  10 , Ilya Shmulevich  2 , Todd Pihl  14 , Ulrike Wagner  14 , Keyvan Farahani  15 , Ron Kikinis  11
Affiliations

NCI Imaging Data Commons

Andrey Fedorov et al. Cancer Res. .

Abstract

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

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Figures

Figure 1. High-level diagram of relevant components of the Imaging Data Commons and related entities, and their relation to the steps of the envisioned CRDC user flow with the emphasis on imaging applications. Green boxes correspond to the envisioned user flow. IDC Extract Transform Load (ETL) process maintains the content of the data collected by external entities (e.g., TCIA) colocated with the various cloud-based tools, such as those maintained by Cloud Resources or by the Google Cloud Platform. The data can be accessed using both the interactive components (e.g., IDC Portal and Viewer) and programmatic APIs.
Figure 1.
High-level diagram of relevant components of the Imaging Data Commons and related entities, and their relation to the steps of the envisioned CRDC user flow with the emphasis on imaging applications. Green boxes correspond to the envisioned user flow. IDC Extract Transform Load (ETL) process maintains the content of the data collected by external entities (e.g., TCIA) colocated with the various cloud-based tools, such as those maintained by Cloud Resources or by the Google Cloud Platform. The data can be accessed using both the interactive components (e.g., IDC Portal and Viewer) and programmatic APIs.
Figure 2. Elements of IDC Portal user interface. Left, front page of the IDC Portal for the pilot (preproduction) release of the platform, available at https://imaging.datacommons.cancer.gov. Right, example of filters available for defining cohort based on the attributes describing segmentation results available in IDC.
Figure 2.
Elements of IDC Portal user interface. Left, front page of the IDC Portal for the pilot (preproduction) release of the platform, available at https://imaging.datacommons.cancer.gov. Right, example of filters available for defining cohort based on the attributes describing segmentation results available in IDC.
See this image and copyright information in PMC

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