Hello, I am Andrey Fedorov from the Imaging Data Commons team.
In the previous videos, we showed how to use the IDC portal to build cohorts, and how to
access the files corresponding to a cohort. In this video you will learn how Google DataStudio
can be used to build customized dashboards for your cohorts.
Imaging Data Commons, or IDC, is a data repository within the NCI Cancer Research Data Commons,
or CRDC.
Note that the functionality discussed in this video corresponds to the pilot, or pre-production
release of IDC, and may change in the subsequent releases of the platform.
IDC portal allows you to build cohorts utilizing a relatively small subset of metadata attributes
available for the data collections hosted by IDC.
Sometimes you may want to further explore your cohort to better understand attributes
of the data that are not available in the IDC portal.
As an example, as of recording this video, the IDC portal does not allow to filter by
the Manufacturer model or image resolution, just as some examples.
Fortunately, there are several approaches for in-depth exploration of the cohort metadata
using the tools readily available within the Google Cloud Platform.
Google DataStudio is a tool that allows you to build custom dashboards and explore data
from various data sources, including BigQuery tables.
Note that the steps covered in this video are described in IDC documentation.
Here we assume you already have a cohort you would like to analyze using a custom dashboard.
You will need to log into IDC with your google account and export the manifest for your cohort
into a BigQuery table.
Next, start with the template dashboard we provide, you will find in the documentation
accompanying IDC.
The template dashboard operates on the data defined by a query, which by default selects
all of the data in IDC.
You will need to update the source of the data to point to the BigQuery table corresponding
to the exported manifest of your cohort.
This query will select all of the DICOM metadata available for your cohort from the BigQuery
table maintained by IDC,and will make those attributes available for exploration in the
dashboard. Choose BigQuery custom query as the connector option, then copy the sample
query from the documentation and replace the placeholder for the manifest cohort table
with the name of the table corresponding to the cohort you want to explore.
Once you update the source of the data, this custom dashboard will provide you with the
summary of the metadata corresponding to your cohort.
As you can see, the dashboard links back to the IDC Viewer for visualization, so you can
provide the user of the dashboard with the experience similar to that in the IDC Portal.
Now that you have the dashboard that operates on your cohort, you can further edit it to
examine any of the DICOM attributes available, and share it either publicly or with specific
users.
As an example, to create a chart summarizing values of the SliceThickness attribute, you
could add a new chart to the dashboard, and choose SliceThickness as the dimension for
this chart. In this example, metric values is set to provide the count of distinct values
of SeriesInstanceUID, so that the chart summarizes the number of distinct imaging series that
have specified SliceThickness values.
You can keep the dashboard you created private, publicly visible, or share with the specific
users. Itâ€™s very important to remember that
the table with the manifest exported to BigQuery will remain available only for 7 days after
creation! If you want to work with your dashboard for longer than that, first make a copy of
the temporary BigQuery table for persistent access!
Remember - 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 from
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.
You will find pointers to documentation and user forum at the IDC portal.
Thank you for your attention, and we look forward to supporting your cancer imaging
research needs with Imaging Data Commons.