“Specify” Scope Description - QI-Bench

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Scope Description


June 2011

Rev
0.1






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



































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Table of Contents

1.

EXECUTIVE SUMMARY

................................
................................
................................

3

1.1.

A
PPLICATION
P
URPOSE

................................
................................
................................
..........

3

1.2.

A
PPLICATION
S
COPE

................................
................................
................................
..............

3

1.3.

T
HE REASON WHY THE AP
PLICATION IS NECESSA
RY

................................
...............................

4

1.4.

T
ERMS
U
SED IN
T
HIS
D
OCUMENT

................................
................................
..........................

5

2.

PROFILES
................................
................................
................................
...........................

6

2.1.

I
NFORMATION
P
ROFILES

................................
................................
................................
........

7

2.2.

F
UNCTIONAL
P
ROFILES

................................
................................
................................
..........

9

2.3.

B
EHAVIORAL
P
ROFILES

................................
................................
................................
.........

9

3.

CONFORMANCE ASSERTIO
NS

................................
................................
....................

9

4.

R
EFERENCES

................................
................................
................................
....................

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1.

Executive Summary

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 th
erapy, 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. Ena
bled by the major
improvements in clinical imaging, the possibility of developing
quantitative

biomarkers is
emerging. For this document “Biomarker” will be used to refer to the
measurement

derived from an imaging method, and “device” or “test” refers to t
he
hardware/software

used to generate the image and extract the measurement.

Regulatory approval for clinical use
1

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 pro
posed as a methodological framework by which stakeholders may work
together.

1.1.

Application Purpose

The purpose of the QI
-
Bench project is to aggregate evidence relevant to the process of
implementing imaging biomarkers to allow sufficient quality and
quantity of data are
generated to support the responsible use of these new tools in clinical settings. The
efficiencies that follow from using this approach could translate into defined processes
that can be sustained to develop and refine imaging diagnost
ic and monitoring tools for
the healthcare marketplace to enable sustained progress in improving healthcare
outcomes. Specifically, the “
Specify”

app is developed to allow users to:




Specify context for use and assay methods.



Use consensus terms in doing

so.

1.2.

Application
Scope

From a technology point of view,
Specify refers to the part of the project most closely
associated with Stanford, comprising the Protégé, BioPortal, and Ruby
-
on
-
Rails
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application that build the internal representation to specify
quantitative imaging
biomarkers and assay methods.


This representation is used downstream.


Our ideas
associated with the “Profile Editor” live here (which itself derives from the name for
QIBA profiles but which has generalized beyond that now).

Most lit
erally,
Specify

would be 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 functionali
ty for more sophisticated users.

1.3.

The reason why the application is n
ecessary

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 quantificat
ion 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 process
es 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 imagi
ng is the extraction of quantifiable features from medical
images for the assessment of normal or the severity, degree of change, or status
of a disease, injury, or chronic condition relative to normal. Quantitative imaging
includes the development, standa
rdization, 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 wi
th 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 i
n 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
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required to address the various sources of uncertainty but the commitment is t
o
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 anal
yses 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 for
mally 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.
QI
-
Bench’s „Specify“ app

uses BioPortal as its
repository of ontologies;

BioPortal encapsulates disparate ontologies and related
annotated data in one common interface available via REST Web service, and
QI
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Bench’s „Specify“ app

builds on top of these services. Therefore,
QI
-
Bench’s „Specify“
app

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.

We have addre
ssed some of the principal shortcomings of present methods


especially
by providing a mechanism to perform and aggregate statistical validation results that are
coupled to controlled vocabularies and that provides a rigorous framework in which to
consider

how general the clinical context may be. We are deploying our tool as a web
service to enable the research community to utilize it as a resource statistical validation
in domains beyond just expression analysis.

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

BRIDG

Biomedical Research Integrated Domain Group

caBIG


Cancer Biomedical Inform
atics 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

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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 Me
dical Language System

VCDE


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 applicati
on 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:

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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 busi
ness 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 a
pplication
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



Diff
erent 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. Nev
ertheless,
profiles may be an effective means of creating groupings of components that make
sense within the larger application concept.

2.1.

Information Profiles


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
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Informatics Services for Quantitative
Imaging
links several relevant concepts
that are distributed acro
ss the conceptual hierarchy.
As such, a spanning ontology that
draws together these concepts is possible using
according to the following princi
ples:



Metadata is data



Annotation is data



Data should be structured



Data models should be defined



Annotation may often follow a model from another domain



Data of all these forms is valuable

Specifically, the domain includes linked m
odels
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
:

Ontolog
y

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 run
-
time

Grammar for specifying
clinical context and
indications for use

RadLex (including
Playbook)

RSNA through
www.radlex.org
,or NCBO
Bioportal

Use

Dynamically
read at run
-
time

Grammar for representing
imaging activities


Gene Ontology (GO)

GO Consortium,
www.geneon
tology.org

Use

Dynamically
read at run
-
time

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 run
-
time

Grammar for representing
imaging biomarkers

Table
1
: Ontologies utilized in meeting the functionality

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
inclu
ded ontologies as a design consideration.


Information models:

Information Model

Available
Extend
Dynamic
Example of use

population and clinical context f
or use.

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through

or just
use?

connection?

Biomedical Research
Integrated Domain Group
(BRIDG) (drawing in HL7
-
RIM 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 fo
r representing multi
-
scale 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 RM
-
ODP Viewpoints, i.e.
Enterprise, In
formation, 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 Confo
rmance 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 increas
ingly specific).
They are not,
however, extensible in and of themselves.

4.

References




1

http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfc
fr/CFRSearch.cfm?CFRPart=8
20&showFR=1
, accessed 28 February 2010.

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2


http://www.rsna.org/Research/TQI/upload/Workshop
-
Summary
-
FINAL.pdf
, accessed
17 March 2010.

3


Giger, QIBA newsletter, February 2010.

4


Giger M, Update on the potential of computer
-
aided diagnosis for breast disease,
Future Oncol. (2010) 6(1), 1
-
4.