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Informatics methods to enable sharing of quantitative imaging research data.

Mia A Levy¹, John B Freymann, Justin S Kirby

¹Department of Biomedical Informatics and Medicine, Division of Hematology and Oncology, Vanderbilt University School of Medicine, Nashville, TN 37232-6838, USA. mia.levy@vanderbilt.edu

Magnetic Resonance Imaging

|July 10, 2012

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The National Cancer Institute Quantitative Research Network (QIN) addresses tool variability in quantitative imaging research by proposing a shared data system. This facilitates data sharing and reuse, accelerating research and biomarker development.

Area of Science:

Medical Imaging
Computational Biology
Biostatistics

Background:

The National Cancer Institute Quantitative Research Network (QIN) aims to accelerate quantitative imaging research through data, algorithm, and tool sharing.
Variability in quantitative imaging tools and platforms presents a challenge to collaborative research and data reuse.
Understanding this variation is crucial for developing effective data sharing strategies.

Purpose of the Study:

To assess the extent of variation in tools and platforms used for quantitative imaging research within the QIN.
To develop a system architecture enabling data sharing and collaborative experimentation among QIN members.
To identify gaps in existing systems and standards for data sharing in the context of QIN research.

Main Methods:

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A survey was conducted among QIN member sites to document current tools for data representation and storage, including images, metadata, and clinical data.
Existing data sharing systems and standards were evaluated for their suitability and limitations for the QIN use case.
A general information system architecture was proposed to support the QIN's data sharing and collaborative research goals.

Main Results:

Significant variation in tools and platforms was observed across QIN institutions.
A general information system architecture was developed to facilitate data sharing and collaborative research within the QIN.
Key gaps in the current architecture were identified, highlighting areas for future development to enable network-wide sharing of research images and metadata.

Conclusions:

Pooling data, algorithms, and tools within the QIN network is expected to stimulate quantitative imaging research.
Current functional requirements present gaps that necessitate future informatics development for seamless data integration.
Technical requirements for translating quantitative imaging methods into clinical research workflows are critical for biomarker validation and qualification.