Hello everybody, and welcome. My name is Dennis. I'm a research scholar at
the Harvard Artificial Intelligence in Medicine program, or AIM, participating
in IDC to explore the integration of AI-based imaging analysis pipelines in the platform.
In the previous videos, we showed how to use the IDC portal to build cohorts, how to access
the files corresponding to a cohort, and how to build customized dashboards for your cohorts.
In this brief video, we will show you one of the use cases we developed so far,
and explain to you how the IDC cloud infrastructure
can be leveraged to do imaging research
as well as what are the advantages the platform brings to the end user.
This initial use case we developed is a replication
and extension of the study published on PLOS medicine by Hosny and colleagues,
where the authors compared the prognostic power of Deep Learning convolutional neural networks
to radiomics models, in an imaging cohort of non-small-cell lung cancer patients.
The evaluation of the developed models, which is what we are aiming to replicate with IDC,
was carried out on one of the most popular imaging datasets in cancer research:
the non-small-cell lung cancer Radiomics dataset.
This dataset was used by Hugo Aerts and colleagues in their 2014 Nature Communication
work that paved the way for a multitude of other radiomics studies, and it was initially
shared on The Cancer Imaging Archive, or TCIA, and kept updated during the years.
In the replication of this work, we used three main features of the IDC platform:
1. We used the IDC viewer to examine and explore individual studies from the
data cohort before running the analysis. 2. We used big query tables maintained by
IDC that store DICOM metadata for IDC content to select the desired sub-cohorts for the analysis;
3. We ran the analysis on a cloud-based dataset maintained by IDC.
Thanks to the fact that IDC is built upon the Google Cloud Computing platform,
the interaction between these modules is made very smooth by tools like Google Colab and the Jupyter
notebooks running on Google Virtual Machines equipped with just the right hardware. Thus,
the IDC platform makes sure the end user has everything he or she needs to perform imaging
analysis studies on the cloud in one place. The only requisite the user needs to satisfy
in order to use the Google Cloud Computing Platform for most of the analysis tasks,
is to have a billing account configured with a valid payment method. You can do this
without committing your credit card using either the Google-provided cloud credits,
or apply for a cloud credit allocation from IDC to experiment with the platform. Instructions about
applying for such credits are included in the IDC documentation and on the homepage of the portal.
The non-small-cell lung cancer radiomics dataset can be accessed
at the IDC portal by browsing the collections for â€œLungâ€ ,
and can be easily explored using the IDC viewer with just a couple of clicks.
This is a quick demonstration of how easy it is to run a pipeline and share the results with others
using IDC and Google Colab. After downloading the code from the project GitHub repository, we
can decide which data to download using BigQuery queries, and download such sub-cohort from IDC
buckets. Since this operation is done entirely in the cloud, fetching the cohort of choice will
usually only take a few seconds. We can play with the data, fully explore individual studies using
the IDC viewer or writing python code to visualize some area of interest for the processing.
We can run the AI inference pipeline on the free Google Colab instance GPUs,
and finally visualize the results and share the notebook on our GitHub account
or by generating a link to it. This particular Google Colab demo can be found on the IDC GitHub
account, under IDC-examples/nsclc-radiomics The use case we presented in this video is
one of the early use cases we are developing to make sure users know everything necessary
to develop their own imaging analysis application on the IDC platform.
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. Thanks for watching.