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Review
. 2013 Aug;26(4):630-41.
doi: 10.1007/s10278-013-9599-2.

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers

Andrew J Buckler  1 , Tiffany Ting Liu, Erica Savig, Baris E Suzek, Daniel L Rubin, David Paik
Affiliations
Review

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers

Andrew J Buckler et al. J Digit Imaging. 2013 Aug.

Erratum in

  • J Digit Imaging. 2013 Aug;26(4):642. Ouellette, M [removed]; Danagoulian, J [removed]; Wernsing, G [removed]

Abstract

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

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Figures

Fig. 1
Fig. 1
First level classes in the QIBO, organized by the order of appearance in an imaging biomarker experiment
Fig. 2
Fig. 2
Top level classes and their relationships
Fig. 3
Fig. 3
Distribution of classes in each of the top-level terms in the Quantitative Imaging Biomarker Ontology: postprocessing algorithm (20), biological intervention (24 concepts), biomarker use (32 concepts), imaging agent (57 concepts), biological subject (57 concepts), quantitative imaging biomarker (57 concepts), indicated biology (92 concepts), biological target (113 concepts), and imaging instrument (115 concepts)
Fig. 4
Fig. 4
Circular graph of the QIBO to get an overview of the nine classes. Upper classes are highlighted in yellow and each node is a class
Fig. 5
Fig. 5
A partial view of the QIBO in Protégé
Fig. 6
Fig. 6
The disease class references equivalent classes in the disease ontology via disease ontology ID using the annotation property
Fig. 7
Fig. 7
An example of using the Quantitative Imaging Biomarker Ontology for novel biomarker discovery. a Each of the two associations was identified from a curated paper. b The two associations can be linked by the common target
See this image and copyright information in PMC

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