This document discusses radiomics research and public databases. It describes what radiomics is and why data sharing is important. Several public databases are mentioned, with an in-depth look at The Cancer Imaging Archive (TCIA). TCIA hosts radiology data like CT, MR, PET images along with associated data. It provides services to upload and access data and enables data citation. Future directions discussed include standardization initiatives and using cloud computing.

1. DEPARTMENT OF HEALTH AND HUMAN SERVICES • National Institutes of Health • National Cancer Institute
Frederick National Laboratory is a Federally Funded Research and Development Center operated by Leidos Biomedical Research, Inc., for the National Cancer Institute
Public Databases for Radiomics Research:
Current Status and Future Directions
Presenter: Justin Kirby
Email: justin.kirby@nih.gov

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What is radiomics?
“With high-throughput computing, it is now possible to rapidly extract innumerable
quantitative features from tomographic images (computed tomography [CT],
magnetic resonance [MR], or positron emission tomography [PET] images).”

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Why is data sharing important for radiomics research?
Given enough data, you will eventually find a correlation with something…

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Many motivations for sharing radiomics data
• Increase research transparency and catch potential mistakes
• Enables re-use for secondary research aims (e.g. same images/ROIs, new features)
• Test your feature stability against data from other institutions
– Different patient populations
– Different scanners
– Different acquisition protocols
• Combine multiple data sets to generate more statistically robust results
• Significantly cheaper than acquiring new data
• Create benchmark data sets to objectively compare analytic performance
• Making biomedical imaging data more accessible to non-medical image processing experts
• Because you’re required to…

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Funding requirements to share data
• National Institutes of Health (NIH) data sharing plan required for grants higher
than $500K/year in direct support
• Numerous additional requirements from NIH programs which require depositing
data in specific databases:
– https://www.nlm.nih.gov/NIHbmic/nih_data_sharing_policies.html

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Publisher requirements to share data
• More journals starting to ask submitters to make their raw data accessible
• Recommended repositories to help people find a home for their data
– Elsevier “Health and Medical Sciences”
– Springer “Health Sciences”
– PLoS One “Biomedical Sciences”
• Data descriptor articles to give academic credit to people who share data
– Nature Scientific Data “Health Sciences”
– Medical Physics (see section 13-Medical Physics Dataset Articles)

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Professional societies focus on data sharing via challenges
https://grand-challenge.org/All_Challenges/

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Data sharing initiatives – Data sets
1. NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories
2. Neuroimaging Informatics Tools and Resources Clearinghouse
3. XNAT Central (518 projects, 5079 subjects, and 7766 imaging sessions of user-contributed data)
4. Image Data Archive (Neurosciences)
5. Federal Interagency Traumatic Brain Injury Research (FITBIR)
6. Pediatric MRI Data Repository
7. National Database for Autism Research
8. Public Lung Database to Address Drug Response
9. The Cancer Imaging Archive (TCIA)

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In depth example: The Cancer Imaging Archive (TCIA)
• Covers most modalities (CT/MR/PET/RT)
• Wide variety of cancers + phantoms
• 40,000 subjects, with patient populations
vary from a handful to >26,000 (NLST)
• Many have associated supporting data
– Demographics/outcomes/therapy
– Pathology imaging
– Radiologist and automated computational
analyses (segmentations, features, etc)
– ‘Omics via GDC, CPTAC, and GEOhttp://www.cancerimagingarchive.net

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TCIA Services – a key component for successful data sharing
• Relieves PI of majority of data sharing burden/risks
– Data hosting with >99% uptime
– De-identification using pre-configured RSNA’s Clinical Trials Processor (CTP) and DICOM PS 3.15
Annex E standards
– Multi-tiered QC process inspects both DICOM headers and pixels for PHI and integrity of data set
• Phone/email support available for end users and submitters
• Extensive documentation throughout the site
• Exposure to a large community of researchers
– Increase visibility of your work, get more citations!

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TCIA Data types – DICOM by itself is not that interesting
Analysis Data (image features)Primary Data (radiology, pathology, clinical, etc)

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TCIA: Publishing primary and analysis data to enable data citations
Analysis Dataset Citation (derived image features)
Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T,
Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat P, Kleper V, Freymann J,
Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. (2014). MR Imaging
Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set.
The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014.4HTXYRCN
Publication Citation (cites specific data used)
MR imaging predictors of molecular profile and survival: multi-institutional
study of the TCGA glioblastoma data set. Radiology. 2013 May;267(2):560-9.
doi: 10.1148/radiol.13120118. Epub 2013 Feb 7. PubMed PMID:
23392431; PubMed Central PMCID: PMC3632807.
Primary Data Citation (TCIA images used for study)
Scarpace, L., Mikkelsen, T., Cha, soonmee, Rao, S., Tekchandani, S., Gutman, D., …
Pierce, L. J. (2016). Radiology Data from The Cancer Genome Atlas Glioblastoma
Multiforme [TCGA-GBM] collection. The Cancer Imaging
Archive. http://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9

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TCIA: Browsing/Searching data

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TCIA: Browsing/Searching data

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TCIA: REST API for automating radiomics pipelines
https://wiki.cancerimagingarchive.net/x/NIIiAQ

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TCIA: Data Analysis Centers to enable collaboration
• DACs are tools or websites which provide
advanced capabilities for
downloading, visualizing, or
analyzing TCIA data
• DACs are not funded by TCIA, but serve
as a construct to enable the research
community to build upon TCIA’s existing
infrastructure (e.g. through ITCR grant
applications)
• TCIA maintains a registry of DACs
to make them discoverable to users
• DACs may provide access to TCIA data
using the API or by mirroring their own
local copy of TCIA data
https://www.slicer.org/wiki/Documentation/4.8/Extensions/TCIABrowser

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TCIA: Community Code Share
• Share source code and scripts
• Uses Github’s “topic” feature

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TCIA: Annotating/Labeling Images for algorithm seed points
Come visit us under the Machine Learning banner in Lakeside!

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Future directions

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BioCADDIE Datamed – “Pubmed for data”
https://datamed.org/

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Standardization initiatives
Standardizing data models and nomenclature for radiomic features

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Cloud Computing
Other Data
Collection
Centers
• NCI’s Cancer Research Data Commons
• Data + tools co-located in a cloud workspace
• Integration with TCIA as a data source

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Acknowledgements
• NCI Cancer Imaging Program
– Paula Jacobs
• Frederick National Laboratory for Cancer Research
– John Freymann, Justin Kirby, Brenda Fevrier-Sullivan, Luis Cordeiro, Craig Hill
• Consultant - Carl Jaffe
• University of Arkansas Medical School
– Fred Prior & the entire TCIA team
• Emory University
– Ashish Sharma

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Questions?
• Justin.Kirby@nih.gov