A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers

Abstract

Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging.

Fig. 1

Information flow schematic of specification and characterization activities of QI-Bench

Fig. 2

Example concepts (as boxes) and relationships (as arrows) from ontologies used by Specify. The direction of arrows is from a service input to an output object; these relations align with RDF triples generated using Specify

Fig. 3

UML Activity Diagram for Specify

Fig. 4

Triples in the specification imply hypotheses that may have been or ultimately will need to be tested. Hypotheses at level 1 is purely technical; can longitudinal volumetry in fact measure TumorSizeChange, and with what bias and precision? Hypotheses at level two layers a clinical validity assertion on top of that, namely, that CytotoxicTreatmentResponse may be measured on this capability. A hypothesis at level 3 layers a clinical utility on top, namely, that CT volumetry in fact is a surrogate endpoint in the stated clinical context for use

Fig. 5

UML Activity Diagram for Formulate

Fig. 6

Image annotations and other derived data may be sourced from outside QI-Bench and retrieved using Formulate, or may be generated by functionality that exists within QI-Bench. In either case, storage objects are linked to the knowledge base via uniform resource identifiers within triples

Fig. 7

Left panel Access to QI-Bench, which is composed of five applications. The first two—Specify and Formulate—are tightly linked and result in specified reference datasets being collected. Downstream applications include Execute, Analyze, and Package. Right panel The current prototype of Specify includes a question-answer paradigm driven capability using BioPortal to create a triple store

Fig. 8

The current prototype of Formulate, which is presently based on caB2B, involves three main components: (1) a set of federated data services, (2) a query engine to run queries against the data services and collect result, and (3) a web application to help create queries and collect the results

Fig. 9

Current QIBO concepts are expanded with those from other relevant models according to links we have identified (example UML representation shown behind the colors)

Fig. 10

Testable hypotheses link to specific datasets used to test them

Fig. 11

Results of the analyses are then used to annotate the knowledge base using W3C “best practices” for “relation strength” via N-ary relations that link triples where the object of the hypothesis triple links to additional triples that represent the method used as well as the result of the computations