Hello, I am Andrey Fedorov from the Imaging Data Commons team,
and in this video I want to give you an overview of the Imaging Data Commons portal.
Imaging Data Commons, or IDC, is a data repository within the NCI Cancer Research Data Commons,
or CRDC. IDC connects researchers with publicly available cancer imaging data,
often linked with other types of cancer data, and co-located with cloud-based computational
resources and big data analysis tools provided by the Google Cloud Platform
In this video we give a brief overview of the Imaging Data Commons (IDC) portal.
IDC Portal is an interactive web application that allows users to explore the data hosted by IDC
and build cohorts, or groups of cases that share a defining characteristics, for subsequent analysis.
As a first step, you can examine the summary of imaging collections available in IDC.
Exploration page provides an interface to explore a subset of metadata attributes
describing the cases available in IDC, and visualize data for the specific cases.
Organization of the images and image-derived data in IDC
follows the DICOM information model. Each case, which corresponds to a Patient in the DICOM model,
will have one or more imaging Studies, which in turn contain one or more imaging Series.
Attributes available for exploration are on the left,
and the collections, cases, studies and series that meet the filter criteria are
listed on the right. Individual series and studies can be visualized in the IDC Viewer.
IDC viewer supports various modes for visualizing images,
and can also be used to explore the annotations associated with the images.
The metadata attributes are organized into three groups.
â€œOriginalâ€ attributes describe the characteristics that are either
common to all or most of types of data in IDC.
â€œDerivedâ€ attributes accompany data types that are derived from images,
such as annotations or analysis results. Finally, â€œRelatedâ€ tab groups the
attributes related to clinical characteristics of the subject or
other types of metadata that may accompany images and is important for the analysis of imaging data.
Metadata attributes can be used to filter IDC content and define subsets, or cohorts of cases.
It is important to remember that IDC cohorts are currently defined as sets of cases, not studies
or series, that satisfy the filter definition. As an example, cases that meet the criteria of
a filter specified as â€œModality equals CTâ€ may also include objects of other modalities.
As an example, we can build a cohort of cases that have CT images,
and that are also accompanied by the segmentation of nodules,
and we can further refine the cohort to include nodules that have volume in the specific range.
Of course, this query can only be performed for the cases that are
accompanied by volumetric segmentations and measurements stored in a standard format,
but the number of collections that include such information is continuously growing.
IDC viewer can then be used to examine the individual images
along with the annotations available for those images.
If you would like to save your cohort, or export it for subsequent analysis, you will need to log
in. You can always come back to the list of the cohorts you saved by accessing your IDC account.
You can also check out our other tutorial videos
to learn more about accessing your cohorts from outside of IDC Portal.
If you would like to use CRDC cloud resources to analyze the data stored in IDC,
you will need to establish a billing account. Free cloud credits are offered to support
imaging research use cases that utilize Google Cloud for the analysis of IDC-hosted data. Please
fill out the form available in IDC documentation to apply and take advantage of this opportunity!
To learn more about IDC, please check out our documentation, and join our user forum to ask
questions and find help from the developers and maintainers of IDC. The forum is monitored by IDC
developers, who will promptly respond to your inquiries. If you have any further questions,
please visit IDC portal for the pointers to the documentation and user forum.
Thank you for your attention, and we look forwar