QI-Bench Specify ASD
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QI-Bench Specify Scope Description
December 2011
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Required Approvals:
Author of this
Revision:
Andrew J. Buckler
Project Manager:
Andrew J. Buckler
Print Name
Signature
Date
Document Revisions:
Revision Revised By
Reason for Update
Date
0.1
AJ Buckler
Initial version
June 2011
0.2
AJ Buckler
Expanded intent and purpose
November 2011
0.3
AJ Buckler
Updated purpose and scope
December 2011
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Table of Contents
1.
EXECUTIVE SUMMARY ................................................................................................ 3
1.1. APPLICATION PURPOSE .......................................................................................................... 4
1.2. APPLICATION SCOPE .............................................................................................................. 6
1.3. THE REASON WHY THE APPLICATION IS NECESSARY............................................................... 6
1.4. TERMS USED IN THIS DOCUMENT .......................................................................................... 7
2.
PROFILES........................................................................................................................... 9
2.1. INFORMATION PROFILES ...................................................................................................... 10
2.2. FUNCTIONAL PROFILES ........................................................................................................ 11
2.3. BEHAVIORAL PROFILES ....................................................................................................... 11
3.
CONFORMANCE ASSERTIONS .................................................................................. 11
4.
REFERENCES .................................................................................................................. 12
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1. Executive Summary
There is a large and growing body of knowledge at the cellular level increasing
utilization of computational methods in drug discovery and development. Likewise, there
is a large and growing body of knowledge at the organism level enabling applications as
computer aided detection, diagnosis, and targeted therapies [1-5]. However, there is
comparatively little available technology to bridge these two bodies of knowledge,
compromising the effectiveness of both. Technology linking these scales through
quantitative analytic processing of acquired imaging and non-imaging data in
translational research [6], coupled with the interpretation of the data through multi-scale
models offers means to comprehend disease processes pre-symptomatically and after
clinical manifestations at multiple levels of abstraction [7-10]. For example, changes in
activities of the receptor tyrosine kinase EGFR can be visualized and quantified through
optical imaging of reconstituted luciferase [8]. Positron Emission Tomography enables
detection and quantification of molecular processes such as glucose metabolism,
angiogenesis, apoptosis and necrosis. Radiolabelled Annexin V molecule uptake by
apoptotic and necrotic cells is developed to measure apoptosis, necrosis and other
disease processes using PET [11, 12]. Chelated gadolinium attached to small peptides
recognizes cell receptors and quantify receptor activities using magnetic imaging
techniques. Similarly, microbubbles and nanobubbbles attached to antibodies such as
anti-P-selectin may be used to image targeted molecules associated with inflammation,
angiogenesis, intravascular thrombus, and tumors [5].
Currently the application of quantitative imaging techniques suffers from the lack of a
standardized representation of image features and content [13-16]. The concept of
“image biobanking” as an analogue to tissue biobanking has great promise [17, 18].
Tools have begun to be available for handling the complexity of genotype [19-23], and
similar advancements are needed in appreciate phenotype, especially as derived from
imaging [15, 24-31]. Publicly accessible resources that support large image archives
provide little more than file sharing and have so far not yet merged into a framework that
supports the collaborative work needed to meet the potential of quantitative imaging
analysis. With the availability of tools for automatic ontology-based annotation of
datasets with terms from biomedical ontologies, coupled with image archives and
means for batch selection and processing of image and clinical data, we believe that
imaging will go through a similar increase in capable analogous to what advanced
sequencing techniques have brought to molecular biology.
Imaging biomarkers are developed for use in the clinical care of patients and in the
conduct of clinical trials of therapy. In clinical practice, imaging biomarkers are intended
to (a) detect and characterize disease, before, during or after a course of therapy, and
(b) predict the course of disease, with or without therapy. In clinical research, imaging
biomarkers are intended to be used in defining endpoints of clinical trials. A precondition
for the adoption of the biomarker for use in either setting is the demonstration of the
ability to standardize the biomarker across imaging devices and clinical centers and the
assessment of the biomarker’s safety and efficacy. Currently qualitative imaging
biomarkers are extensively used by the medical community. Enabled by the major
improvements in clinical imaging, the possibility of developing quantitative biomarkers is
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emerging. For this document “Biomarker” will be used to refer to the measurement
derived from an imaging method, and “device” or “test” refers to the hardware/software
used to generate the image and extract the measurement.
Regulatory approval for clinical use1 and regulatory qualification for research use
depend on demonstrating proof of performance relative to the intended application of
the biomarker:




In a defined patient population,
For a specific biological phenomenon associated with a known disease state,
With evidence in large patient populations, and
Externally validated.
The use of imaging biomarkers occurs at a time of great pressure on the cost of medical
services. To allow for maximum speed and economy for the validation process, this
strategy is proposed as a methodological framework by which stakeholders may work
together.
1.1. Application Purpose
Driving biological questions addressed by this proposed work include questions
associated with formal imaging biomarker qualification such as “is CT volumetry better
than unidimensional measures for RECIST assessment of cancer response?”, or “is
FDG-PET a surrogate marker for survival?” Biological research questions include “can
we find a method to compare preclinical and clinical imaging?” “Can we find quantitative
links between imaging mouse and man?” “Can we optimize the frequency of serial
imaging?” “What is the influence of location on tumor development?” “Can we build a
preclinical in vivo imaging database with translational value, such as supporting the
Mouse Models of Human Cancer Center (MMHCC)?” “How well can we compare
across imaging modalities?” Each of these problem areas ultimately depends on precise
identification of the concepts in each question and an ability to bring data resources to
bear that can help answer the question.
QI-Bench is created to aggregate large-scale datasets of evidence relevant to
characterizing and optimizing imaging biomarkers. We are developing resources that
enable many parties to better utilize available data and a neutral broker resource that
will provide developers and regulators with unbiased and objective data demonstrating
imaging biomarker efficacy. QI-Bench is composed of five linked applications, (1)
Specify (detailed description of the context for use and assay specifics), (2) Formulate
(gather relevant datasets), (3) Execute (perform batch image analyses), (4) Analyze
(statistical analysis of image analyses), and (5) Package (compile evidence for
regulatory filing) downstream (Fig. 1). The vision is to deploy the software both as webaccessible resources for collaborative community effort or as instances that may be
used within individual organizations for their own purposes.
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Figure 1. Left: Access to QI-Bench, which is composed of five linked applications, is at www.qi-bench.org.
Downstream applications include Execute, Analyze, and Package. Right: The current prototype of Specify
includes a question-answer paradigm driven capability (right hand side of the screen) using BioPortal to
create a triple store (showing in left window on screen). The prototype also incorporates the AIM Template
Builder.
Specify creates RDF triples (subject-predicate-object units of knowledge) stored in a
database based on using the QIBO to guide a question-answer paradigm for naturally
interacting with domain experts to create an early version of what this proposal would
turn into W3C-compliant SPARQL endpoints (query interface) [32]. Specifically, Specify
is developed to allow users to:


Specify context for use and assay methods.
Use consensus terms in doing so.
There are four innovative aspects of the proposed work:
1. We make precise semantic specification uncomplicated for diverse groups
of experts that are not skilled proficient in knowledge engineering tools.
The key is to bring a level of rigor to the problem space in such a way as to
facilitate cross-disciplinary teams to function without requiring individuals to be
experts in the representation of knowledge, inferencing mechanisms, or
computer engineering associated with grid computing or database query design.
2. We map both medical as well as technical domain expertise into
representations well suited to emerging capabilities of the semantic web.
The proposed project captures functionality improved from the currently available
infrastructures such as caGrid and data integration approaches supported by
these infrastructures such as caB2B/caIntegrator of caBIG. It explores how a
linked data interface can be created from an object-oriented data interface based
on a UML model, annotated with CDEs according to the ISO-11179 metadata
registry metamodel standard. The experience could illuminate best practices for
combining a semantic web approach on the data interface layer with a modeldriven approach for software development, especially since Common Data
Elements are widely used to annotate Case Report Form (CRF) templates for
clinical research.
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3. We address the problem of efficient use of resources to assess limits of
generalizability. Determining the biological relevance of a quantitative imaging
read-out is a difficult problem. For example, having direct tumor volumetry data in
the lung and the pancreas, do these results extend to the liver? First, it is
important to establish to what extent an intermediate marker is in the causal
pathway to a true clinical endpoint. Second, given the combinatorial complexity
that arises with multiple contexts for use, multiple imaging protocols, etc., a
logical and mathematical framework is needed to establish how extant study data
may be used to establish performance in clinical contexts that were not explicitly
part of the original studies. Existing tools rarely if ever relate the logical world of
ontology with the biostatistical analyses that characterize diagnostic or prognostic
performance. Existing tools do not permit the extrapolation of statistical validation
results to semantically related situations. Despite decades of using statistical
validation approaches, there is no methodology to formally represent the
generalizability of a validation study.
4. We provide this capability in a manner that is accessible to varying levels
of collaborative models, from individual companies or institutions to larger
consortia or public-private partnerships to fully open public access.
1.2. Application Scope
Specify refers to the part of the project most closely associated with Protégé, BioPortal,
and the application code that builds the triple store to specify quantitative imaging
biomarkers and assay methods. This representation is used downstream.
Specify is packaged in two forms: 1) as a web-service linking to the databases on the
project server dev.bbmsc.com; and 2) as a local installation/instance of the functionality
for more sophisticated users.
1.3. The reason why the application is necessary
Application of imaging biomarkers often suffers from the lack of a standardized
interpretation. This is exacerbated by large measurement variability for different
contexts of use. Quantitative imaging capability should allow precise quantification of
clinically relevant features when applied in a precise and optimized fashion with a
defined standard for interpretation of results. Aggregating results from clinical trials or
systematic clinical experience may allow for faster refinement of processes that
minimize measurement variance. This is a complex task since there are many distinct
imaging techniques, platforms, and interpretation schemes. Broad collaboration across
the many relevant stakeholder communities is essential to advance the field, supported
by resources for batch analysis over large image archives.
In 2009, the Toward Quantitative Imaging (TQI) task force of the Radiological Society of
North America (RSNA) developed a working definition of quantitative imaging:2
“Quantitative imaging is the extraction of quantifiable features from medical
images for the assessment of normal or the severity, degree of change, or status
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of a disease, injury, or chronic condition relative to normal. Quantitative imaging
includes the development, standardization, and optimization of anatomical,
functional, and molecular imaging acquisition protocols, data analyses, display
methods, and reporting structures. These features permit the validation of
accurately and precisely obtained image-derived metrics with anatomically and
physiologically relevant parameters, including treatment response and outcome,
and the use of such metrics in research and patient care.”
Such image-derived metrics may involve the extraction of lesions from normal
anatomical background and the subsequent analysis of this extracted region over time,
in order to yield a quantitative measure of some anatomic, physiologic or
pharmacokinetic characteristic. Computational methods that inform these analyses are
being developed by researchers in the field of quantitative imaging, computer-aided
detection (CADe) and computer-aided diagnosis (CADx).3,4 They may also be obtained
using quantitative outputs, such as those derived from molecular imaging.
QIBA and others have already mapped certain sources of imaging variance and have
proposed that standardizing image acquisition and review quality process could greatly
reduce certain types of variance. If this process of standardization can be implemented
throughout the clinical community for the range of imaging techniques, the prospects for
robust imaging biomarkers would be significantly improved. A range of strategies will be
required to address the various sources of uncertainty but the commitment is to
approach this challenge in a systematic fashion in an effort to bring the rigor of
measurement science to imaging biomarker application.
However, these efforts generally do not relate the logical world of ontology development
with the biostatistical analyses that characterize performance. Moreover, existing tools
do not permit the extrapolation of statistical validation results along arbitrary ontology
hierarchies. Despite decades of using statistical validation approaches, there is no
methodology to formally represent the generalizability of a validation activity.
Building upon existing tools and content in NCBO‘s BioPortal, we create a system that
addresses some of these drawbacks. Specify uses BioPortal as its repository of
ontologies; BioPortal encapsulates disparate ontologies and related annotated data in
one common interface available via REST Web service. Specify builds on top of these
services. Specify is able to work with over any ontology in BioPortal – including QIBO
and approximately 200 others. The ontology library is separately curated and updated
by the administrators of BioPortal, decoupling us from the underlying representation and
versioning of the ontologies.
1.4. Terms Used in This Document
The following are terms commonly used that may of assistance to the reader.
AAS
Application Architecture Specification
ASD
Application Scope Description
BAM
Business Architecture Model
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BRIDG
Biomedical Research Integrated Domain Group
caBIG
Cancer Biomedical Informatics Grid
caDSR
Cancer Data Standards Registry and Repository
CAT
Composite Architecture Team
CBIIT
Center for Biomedical Informatics and Information Technology
CFSS
Conceptual Functional Service Specification
CIM
Computational Independent Model
DAM
Domain Analysis Model
EAS
Enterprise Architecture Specification
ECCF
Enterprise Conformance and Compliance Framework
EOS
End of Support
ERB
Enterprise Review Board
EUC
Enterprise Use-case
IMS
Issue Management System (Jira)
KC
Knowledge Center
NCI
National Cancer Institute
NIH
National Institutes of Health
PIM
Platform Independent Model
PSM
Platform Specific Model
PMO
Project Management Office
PMP
Project Management Plan
QA
Quality Assurance
QSR
FDA’s Quality System Regulation
SAIF
Service Aware Interoperability Framework
SDD
Software Design Document
SIG
Service Implementation Guide
SUC
System Level Use-case
SME
Subject Matter Expert
SOA
Service Oriented Architecture
SOW
Statement of Work
UML
Unified Modeling Language
UMLS
Unified Medical Language System
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Vocabularies & Common Data Elements
When using the template, extend with specific terms related to the particular EUC being
documented.
2. Profiles
A profile is a named set of cohesive capabilities. A profile enables an application to be
used at different levels and allows implementers to provide different levels of
capabilities in differing contexts. Whereas interoperability is the metric with services,
applications focus on usability (from a user’s perspective) and reusability (from an
implementer’s).
Include the following three components in each profile:

Information Profile: identification of a named set of information descriptions (e.g.
semantic signifiers) that are supported by one or more operations.

Functional Profile: a named list of a subset of the operations defined as
dependencies within this specification which must be supported in order to claim
conformance to the profile.

Behavioral Profile: the business workflow context (choreography) that fulfills one
or more business purposes for this application. This may optionally include
additional constraints where relevant.
Fully define the profiles being defined by this version of the application.
When appropriate, a minimum profile should be defined. For example, if an application
provides access to several business workflows, then one or more should be deemed
essential to the purpose of the application.
Each functional profile must identify which interfaces are required, and when relevant,
where specific data groupings, etc… are covered etc.
When profiling, consider the use of your application in:

Differing business contexts

Different localizations

Different information models

Partner-to-Partner Interoperability contexts

Product packaging and offerings
Profiles themselves are optional components of application specifications, not
necessarily defining dependencies as they define usage with services. Nevertheless,
profiles may be an effective means of creating groupings of components that make
sense within the larger application concept.
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2.1. Information Profiles
Informatics Services for Quantitative
Imaging links several relevant concepts
that
are
distributed
across
the
conceptual hierarchy. As such, a
spanning ontology that draws together
these concepts is possible using
according to the following principles:
• Metadata is data
• Annotation is data
• Data should be structured
• Data models should be defined
• Annotation may often follow a
model from another domain
•
Figure 1: The semantic infrastructure needed to
support
quantitative
imaging
performance
assessment encompasses multiple related but
distinct concepts and vocabularies to represent
them which include characterization of the target
population and clinical context for use.
Data of all these forms is valuable
Specifically, the domain includes linked models and controlled vocabularies for the
categories identified in Figure 14.
In order to support these capabilities, the following strategy will be employed in the
development and/or use of information models and ontologies (Tables 2 and 3):
Ontologies:
Ontology
Available through
Extend
or just
use?
Dynamic
connection?
Example of use
Systematized
Nomenclature of
Medicine--Clinical Terms
(SNOMED-CT)
UMLS Metathesaurus,
NCBO BioPortal
Use
Dynamically
read at runtime
Grammar for specifying
clinical context and
indications for use
RadLex (including
Playbook)
RSNA through
www.radlex.org,or NCBO
Bioportal
Use
Dynamically
read at runtime
Grammar for representing
imaging activities
Gene Ontology (GO)
GO Consortium,
www.geneontology.org
Use
Dynamically
read at runtime
Nouns for representing
genes and gene products
associated with
mechanisms of action
International vocabulary of
metrology --- Basic and
general concepts and
associated terms (VIM)
International Bureau of
Weights and Measures
(BIPM)
Use
Updated on
release
schedule
How to represent
measurements and
measurement uncertainty
Exploratory imaging
biomarkers
Paik Lab at Stanford
Extend
Dynamically
read at runtime
Grammar for representing
imaging biomarkers
Table 1: Ontologies utilized in meeting the functionality
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As a practical matter, many (but not all) of these ontologies have been collected within
the NCI Thesaurus (NCIT). It may be that there is utility in utilizing this to subsume
included ontologies as a design consideration.
Information models:
Information Model
Available
through
Extend
or just
use?
Dynamic
connection?
Example of use
Biomedical Research
Integrated Domain Group
(BRIDG) (drawing in HL7RIM and SDTM)
caBIG
Use
Updated on
release
schedule
Data structures for clinical trial steps
and regulatory submissions of
heterogeneous data across imaging
and non-imaging observations
Life Sciences Domain
Analysis Model (LS-DAM)
caBIG
Use
Updated on
release
schedule
Data structures for representing multiscale assays and associating them
with mechanisms of action that link
phenotype to genotype
Annotation and Image
Markup (AIM)
caBIG
Extend
Updated on
release
schedule
Data structures for imaging phenotypes
Table 2: Information Models utilized in meeting the functionality
2.2. Functional Profiles

A named list of a subset of the operations, defined as dependencies within this
specification, which must be supported in order to claim conformance to the
profile.
2.3. Behavioral Profiles

The business workflow context (choreography) that fulfills one or more business
purposes for this application. This may optionally include additional constraints
where relevant.
3. Conformance Assertions
Conformance Assertions are testable, verifiable statements made in the context of a
single RM-ODP Viewpoint (ISO Standard Reference Model for Open Distributed
Processing, ISO/IEC IS 10746|ITU-T X.900). They may be made in four of the five RMODP Viewpoints, i.e. Enterprise, Information, Computational, and/or Engineering. The
Technology Viewpoint specifies a particular implementation /technology binding that is
run within a ‘test harness’ to establish the degree to which the implementation is
conformant with a given set of Conformance Assertions made in the other RM-ODP
Viewpoints. Conformance Assertions are conceptually non-hierarchical. However,
Conformance Assertions may have hierarchical relationships to other Conformance
Assertions within the same Viewpoint (i.e. be increasingly specific). They are not,
however, extensible in and of themselves.
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