Monthly Program UpdateMarch 8, 2012
Andrew J. Buckler, MSPrincipal Investigator
WITH FUNDING SUPPORT
PROVIDED BY NATIONAL
INSTITUTE OF STANDARDS AND
TECHNOLOGY
Agenda
• Summary and close-out of the „Winter 2012“ development iteration– Covering what’s been accomplished from
multiple points of view
• Preview of „Spring 2012“ development iteration– With focus on directions in
StudyDescription and „ISA“ storage model, evaluation of workflow engine.
22
3
Winter 2012 (n=47)Autumn 2011 (n=19)
Spring 2012 (n=32) Unstaged (n=19)
Overal
l
Speci
fy
Form
ulate
Execu
te
Analyze
Packag
e
Studies
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OpenResolved
Overal
l
Speci
fy
Form
ulate
Execu
te
Analyze
Packag
e
Studies
Unresolve
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OpenResolved
Overal
l
Speci
fy
Form
ulate
Execu
te
Analyze
Packag
e
Studies
02468
10121416
OpenResolved
Overal
l
Speci
fy
Form
ulate
Execu
te
Analyze
Packag
e
Studies
02468
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OpenResolved
333
• Ramp-up of formal development environment, (including issue tracking)
• Initial Specify (including QIBO and Knowledgebase)
• Major update to Execute: Metadata extraction Better Batchmake GUI
• Initial Formulate• Specify now creates
instances in knowledgebase
• Change studies• Scripted reader studies• Export to Analyze• Import from Formulate• Evaluate workflow
application “Iterate”
• Clojure DSL for executable specifications
• Major update to Analyze• RDF-compliance in Specify• Formulate using SPARQL• Service APIs for Iterate
3A Pilot
3A Pivotal
ISA Storage Model
Analyze project
Specify/ Formulate
project
User: Lab Protocol• Develop and run queries based on data
requirements – Use of Formulate
• Load Reference Data into the Reference Data Set Manager
– Example Pilot3A Data Processing Steps
• Server-Side Processing using the Batch Analysis Service
– Package Algorithm or Method using Batch Analysis Service API
– Prepare Data Set • Create Ground Truth or other Reference Annotation
and Markup • Importing location points and other data for use
– Writing Scripts – Initiate a Batch Analysis Run
• Perform statistical analysis – Analyze Use Instructions
Developer: Design Documents• User Needs and Requirements Analysis • Architecture • Application-specific Design
– Specify • "Specify" Scope Description (ASD) • "Specify" Architecture Specification (AAS) • "Quantitative Imaging Biomarker Ontology (QIBO)" Softwa
re Design Document (SDD)
• "Biomarker DB" (a.k.a., the triple store) Software Design Document (SDD)
• AIM Template Builder Design Documentation:
– Formulate • "Formulate" Scope Description (ASD) • "Formulate" Architecture Specification (AAS) • "NBIA Connector" Software Design Document (SDD)
– Execute • "Execute" Scope Description (ASD) • "Execute" Architecture Specification (AAS) • Reference Data Set Manager (RDSM) Software Design Doc
ument (SDD)
• Batch Analysis Service Software Design Document (SDD)
– Analyze • "Analyze" Scope Description (ASD) • "Analyze" Architecture Specification (AAS)
– Package • "Package" Scope Description (ASD) • "Package" Architecture Specification (AAS)
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(Form of) Early Analysis Results
3A Challenge Series1. Median Technologies2. Vital Images, Inc.3. Fraunhofer Mevis4. Siemens5. Moffitt Cancer Center6. Toshiba
5555
Pilot
Pivotal
Investigation 1
Train
Test
Pilot
Pivotal
Investigation
Train
Test
Pilot
Pivotal
Investigation
Train
Test
Pilot
Pivotal
Investigation n
Train
Test
Pr im
ar y
Seco n
dar y
• Defined set of data• Defined challenge• Defined test set policy
First Participants7. GE Healthcare8. Icon Medical Imaging9. Columbia University10. INTIO, Inc.11. Vital Images, Inc.
Standardized Representation of Quantitative Imaging
Statistical Validation Services for Quantitative Imaging
66666
OK. Now into the details for Spring 2012 Iteration: Starting with what we said in January…
7777
Formulate
Statistical Analysis Results (Relation
strength)
Annotation and Image Markup,
Non-imaging Clinical Data
Primary Data: Images and other
Raw Data
Reference Data SetsQIBO
Specify
RDF Triple Store
CT Volumetry CT
obtained_by
Tumor growth
measure_of
TherapeuticEfficacy
used_for
Analyze
Y=β0..n+β1(QIB)+β2T+ eij
Execute
Feedbac k
Feed
bac
k
…and where we left off in February…// Business RequirementsFNIH, QIBA, and C-Path participants don’t have a way to provide precise specification for
context for use and applicable assay methods (to allow semantic labeling):BiomarkerDB = Specify (biomarker domain expertise, ontology for labeling);
Researchers and consortia don’t have an ability to exploit existing data resources with high precision and recall:ReferenceDataSet+ = Formulate (BiomarkerDB, {DataService} );
Technology developers and contract research organizations don’t have a way to do large-scale quantitative runs:ReferenceDataSet .CollectedValue+ = Execute (ReferenceDataSet.RawData);
The community lacks way to apply definitive statistical analyses of annotation and image markup over specified context for use:BiomarkerDB.SummaryStatistic+ = Analyze ( { ReferenceDataSet .CollectedValue } );
Industry lacks standardized ways to report and submit data electronically:efiling transactions+ = Package (BiomarkerDB, {ReferenceDataSet} );
888888
Rotate it to align with the horizontal rather than vertical presentation of our splash screen…
// B
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999999
…to arrive at a new more complete view(interpreting the braces as a separate application)
// B
usin
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Requ
irem
ents
FNIH
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A, a
nd C
-Pat
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101010101010
Worked Example (starting from claim analysis we discussed in February 2011)
Measurements of tumor volume are more precise (reproducible) than uni-dimensional tumor measurements of tumor diameter. Longitudinal changes in whole tumor volume during therapy predict clinical outcomes (i.e., OS or PFS) earlier than corresponding uni-dimensional measurements. Therefore, tumor response or progression as determined by tumor volume will be able to serve as the primary endpoint in well-controlled Phase II and III efficacy studies of cytotoxic and selected targeted therapies (e.g., antiangiogenic agents, tyrosine kinase inhibitors, etc.) in several solid, measurable tumors (including both primary and metastatic cancers of, e.g., lung, liver, colorectal, gastric, head and neck cancer,) and lymphoma. Changes in tumor volume can serve as the endpoint for regulatory drug approval in registration trials.
Biomarker claim statements are information-rich and may be used to set up the needed analyses.
111111111111
The user enters information from claiminto the knowledgebase using SpecifyMeasurements of tumor volume are more precise (reproducible) than uni-dimensional tumor measurements of tumor diameter. Longitudinal changes in whole tumor volume during therapy predict clinical outcomes (i.e., OS or PFS) earlier than corresponding uni-dimensional measurements. Therefore, tumor response or progression as determined by tumor volume will be able to serve as the primary endpoint in well-controlled Phase II and III efficacy studies of cytotoxic and selected targeted therapies (e.g., antiangiogenic agents, tyrosine kinase inhibitors, etc.) in several solid, measurable tumors (including both primary and metastatic cancers of, e.g., lung, liver, colorectal, gastric, head and neck cancer,) and lymphoma. Changes in tumor volume can serve as the endpoint for regulatory drug approval in registration trials.
Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange
predicts TreatmentResponse
Categoric
Continuous
Continuous
1212
…pulling various pieces of information,
Measurements of tumor volume are more precise (reproducible) than uni-dimensional tumor measurements of tumor diameter. Longitudinal changes in whole tumor volume during therapy predict clinical outcomes (i.e., OS or PFS) earlier than corresponding uni-dimensional measurements. Therefore, tumor response or progression as determined by tumor volume will be able to serve as the primary endpoint in well-controlled Phase II and III efficacy studies of cytotoxic and selected targeted therapies (e.g., antiangiogenic agents, tyrosine kinase inhibitors, etc.) in several solid, measurable tumors (including both primary and metastatic cancers of, e.g., lung, liver, colorectal, gastric, head and neck cancer,) and lymphoma. Changes in tumor volume can serve as the endpoint for regulatory drug approval in registration trials.
Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
<compliant>LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange predicts CytotoxicTreatmentResponse
TyrosineKinaseInhibitor
is CytotoxicTreatment
well-controlled Phase II and III efficacy studies
uses CytotoxicTreatmentResponse
CytotoxicTreatment
influences NonSmallCellLungCancer
CT images Thorax
Thorax contains NonSmallCellLungCancer
Intervention
Target Indication
1313
…to form the specification.
Measurements of tumor volume are more precise (reproducible) than uni-dimensional tumor measurements of tumor diameter. Longitudinal changes in whole tumor volume during therapy predict clinical outcomes (i.e., OS or PFS) earlier than corresponding uni-dimensional measurements. Therefore, tumor response or progression as determined by tumor volume will be able to serve as the primary endpoint in well-controlled Phase II and III efficacy studies of cytotoxic and selected targeted therapies (e.g., antiangiogenic agents, tyrosine kinase inhibitors, etc.) in several solid, measurable tumors (including both primary and metastatic cancers of, e.g., lung, liver, colorectal, gastric, head and neck cancer,) and lymphoma. Changes in tumor volume can serve as the endpoint for regulatory drug approval in registration trials.
To produce data for
registration
To substantiate quality of evidence
development
Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
<compliant>LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange predicts CytotoxicTreatmentResponse
TyrosineKinaseInhibitor is CytotoxicTreatment
well-controlled Phase II and III efficacy studies
uses CytotoxicTreatmentResponse
CytotoxicTreatment influences NonSmallCellLungCancer
CT images Thorax
Thorax contains NonSmallCellLungCancer
regulatory drug approval dependsOn PrimaryEndpoint
well-controlled Phase II and III efficacy studies
assess PrimaryEndpoint
CT Volumetry is <putative>SurrogateEndpoint
1414
Formulate interprets the specification as testable hypotheses,Measurements of tumor volume are more precise (reproducible) than uni-dimensional tumor measurements of tumor diameter. Longitudinal changes in whole tumor volume during therapy predict clinical outcomes (i.e., OS or PFS) earlier than corresponding uni-dimensional measurements. Therefore, tumor response or progression as determined by tumor volume will be able to serve as the primary endpoint in well-controlled Phase II and III efficacy studies of cytotoxic and selected targeted therapies (e.g., antiangiogenic agents, tyrosine kinase inhibitors, etc.) in several solid, measurable tumors (including both primary and metastatic cancers of, e.g., lung, liver, colorectal, gastric, head and neck cancer,) and lymphoma. Changes in tumor volume can serve as the endpoint for regulatory drug approval in registration trials.
Type of biomarker, in this case predictive (could have been
something else, e.g., prognostic), to establish the mathematical
formalism
Technical characteri
stic
Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
<compliant>LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange predicts CytotoxicTreatmentResponse
TyrosineKinaseInhibitor is CytotoxicTreatment
well-controlled Phase II and III efficacy studies
uses CytotoxicTreatmentResponse
CytotoxicTreatment influences NonSmallCellLungCancer
CT images Thorax
Thorax contains NonSmallCellLungCancer
regulatory drug approval dependsOn PrimaryEndpoint
well-controlled Phase II and III efficacy studies
assess PrimaryEndpoint
CT Volumetry is <proven>SurrogateEndpoint
1
3
2
1515
…setting up an investigation (I), study (S), assay (A) hierarchy…Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
<compliant>LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange predicts CytotoxicTreatmentResponse
TyrosineKinaseInhibitor is CytotoxicTreatment
well-controlled Phase II and III efficacy studies
uses CytotoxicTreatmentResponse
CytotoxicTreatment influences NonSmallCellLungCancer
CT images Thorax
Thorax contains NonSmallCellLungCancer
regulatory drug approval dependsOn PrimaryEndpoint
well-controlled Phase II and III efficacy studies
assess PrimaryEndpoint
CT Volumetry is <putative>SurrogateEndpoint
1
3
2
1616
Investigations to Prove the Hypotheses:1. Technical Performance = Biological
Target + Assay Method2. Clinical Validity = Indicated Biology
+ Technical Performance3. Clinical Utility = Biomarker Use +
Clinical Validity
Investigation-Study-Assay Hierarchy:• Investigation = {Summary Statistic} +
{Study}• Study = {Descriptive Statistic} +
Protocol + {Assay}• Assay = RawData + {AnnotationData}• AnnotationData = [AIM file|mesh|…]
…ADDING TRIPLES TO CAPTURE URIs:Subject Predicate Object
ClinicalUtility is Investigation URI
ClinicalValidity is Investigation URI
TechnicalPerformance is Investigation URI
Investigation has SummaryStatisticType
Investigation has Study URI
Study has DescriptiveStatisticType
Study has Protocol URI
Study has Assay URI
Assay has RawData URI
…and loading data into Execute (at least raw data, possibly annotations if they already exist)
Subject Predicate Object
A Is Patient
A isDiagnosedWith DiseaseA
DiseaseA Is NonSmallLCellLunCancer
Pazopanib Is TyrosoineKinaseInhibitor
A hasBaseline CT
A hasTP1 CT
A hasTP2 CT
B isDiagnosedWith DiseaseA
B hasBaseline CT
B hasTP1 CT
A hasOutcome Death
B hasOutcome Survival
1717
DISCOVERED DATA: …LOADING DATA INTO THE RDSM:
Reference Data Set Manager:
Heavyweight Storage with URIs
Knowledgebase:Lightweight
Storage linking to URIs
If no annotations, Execute creates them(in either case leaving Analyze with its data set up for it)
Subject Predicate Object
ClinicalUtility is Investigation URI
ClinicalValidity is Investigation URI
TechnicalPerformance is Investigation URI
Investigation has SummaryStatisticType
Investigation has Study URI
Study has DescriptiveStatisticType
Study has Protocol URI
Study has Assay URI
Assay has RawData URI
Assay has AnnotationData URI
AIM file is AnnotationData URI
Mesh is AnnotationData URI
1818
Either in batch or viaScripted reader studies
(using “Share” and “Duplicate” functions of RDSM to leverage cases across investigations)
(self-generating knowledgebase from RDSM hierarchy and ISA-TAB description files)
Reference Data Set Manager:
Heavyweight Storage with URIs
Knowledgebase:Lightweight
Storage linking to URIs
Analyze performs the statistical analyses…
Subject Predicate Object
A Is Patient
A isDiagnosedWith DiseaseA
DiseaseA Is NonSmallLCellLunCancer
A hasClinicalObservation
B
B Is TumorShrinkage
C Is Patient
C hasClinicalObservation
B
D hasClinicalObservation
B
Pazopanib Is TyrosoineKinaseInhibitor
A isTreatedWith Pazopanib
A hasOutcome Death
C hasOutcome Survival
Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
<compliant>LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange predicts CytotoxicTreatmentResponse
TyrosoineKinaseInhibitor is CytotoxicTreatment
well-controlled Phase II and III efficacy studies
uses CytotoxicTreatmentResponse
CytotoxicTreatment influences NonSmallCellLungCancer
CT images Thorax
Thorax contains NonSmallCellLungCancer
regulatory drug approval dependsOn PrimaryEndpoint
well-controlled Phase II and III efficacy studies
assess PrimaryEndpoint
CT Volumetry is SurrogateEndpoint for CytotoxicTreatment
1
3
2
1919
…and adds the results to the knowledgebase (using W3C “best practices” for “relation strength”).
Subject Predicate Object
45324 biasMethod <r script used>
45324 bias <summary statistic>
45324 variabilityMethod <r script used>
45324 variability <summary statistic>
9956 <correlation>Method <r script used>
9956 correlation <summary statistic>
9956 <ROC>Method <r script used>
9956 ROC <summary statistic>
98234 Effect of treatment on true endpoint <value>
98234 Effect of treatment on surrogate endpoint <value>
98234 Effect of surrogate on true endpoint <value>
98234 Effect of treatment on true endpoint relative to that on surrogate endpoint
<value>
Subject Predicate Object
CT images Tumor
Volumetry analyzes CT
<compliant>LongitudinalVolumetry
estimates TumorSizeChange
TumorSizeChange predicts CytotoxicTreatmentResponse
TyrosoineKinaseInhibitor is CytotoxicTreatment
well-controlled Phase II and III efficacy studies
uses CytotoxicTreatmentResponse
CytotoxicTreatment influences NonSmallCellLungCancer
CT images Thorax
Thorax contains NonSmallCellLungCancer
regulatory drug approval dependsOn PrimaryEndpoint
well-controlled Phase II and III efficacy studies
assess PrimaryEndpoint
CT Volumetry is SurrogateEndpoint for CytotoxicTreatment
1
3
2
URI=45324
URI=9956
URI=98234
2020
PackageStructure submissions according to eCTD, HL7 RCRIM, and SDTM
Section 2 Summaries2.1. Biomarker Qualification Overview2.1.1. Introduction2.1.2. Context of Use2.1.3. Summary of Methodology and Results2.1.4. Conclusion2.2. Nonclinical Technical Methods Data2.2.1. Summary of Technical Validation Studies and Analytical Methods2.2.2. Synopses of individual studies2.3. Clinical Biomarker Data2.3.1. Summary of Biomarker Efficacy Studies and Analytical Methods2.3.2. Summary of Clinical Efficacy [one for each clinical context]2.3.3. Synopses of individual studies
Section 3 Quality<used when individual sponsor qualifies marker in a specific NDA>
Section 4 Nonclinical Reports4.1. Study reports4.1.1. Technical Methods Development Reports4.1.2. Technical Methods Validation Reports4.1.3. Nonclinical Study Reports (in vivo)4.2. Literature references
Section 5 Clinical Reports5.1. Tabular listing of all clinical studies5.2. Clinical study reports and related information5.2.1. Technical Methods Development reports5.2.2. Technical Methods Validation reports5.2.3. Clinical Efficacy Study Reports [context for use]5.3. Literature references
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Subject Predicate Object
45324 biasMethod <r script used>
45324 bias <summary statistic>
45324 variabilityMethod <r script used>
45324 variability <summary statistic>
9956 <correlation>Method <r script used>
9956 correlation <summary statistic>
9956 <ROC>Method <r script used>
9956 ROC <summary statistic>
98234 Effect of treatment on true endpoint <value>
98234 Effect of treatment on surrogate endpoint <value>
98234 Effect of surrogate on true endpoint <value>
98234 Effect of treatment on true endpoint relative to that on surrogate endpoint
<value>
Iterate: Reproducible Workflows with Documented Provenance (with illustration expansion of databases)
22222222222222
Knowledgebase Triples
N N+500 N+1000 N+1000 N+2000 -> eCTD
Reference Data Sets
M M M+1000 M+10,000 M+10,000 -> eCTD
2323
Value proposition of QI-Bench• Efficiently collect and exploit evidence establishing
standards for optimized quantitative imaging:– Users want confidence in the read-outs– Pharma wants to use them as endpoints– Device/SW companies want to market products that produce them
without huge costs– Public wants to trust the decisions that they contribute to
• By providing a verification framework to develop precompetitive specifications and support test harnesses to curate and utilize reference data
• Doing so as an accessible and open resource facilitates collaboration among diverse stakeholders
2424
Summary:QI-Bench Contributions• We make it practical to increase the magnitude of data for increased
statistical significance. • We provide practical means to grapple with massive data sets.• We address the problem of efficient use of resources to assess limits of
generalizability. • We make formal specification accessible to diverse groups of experts that are
not skilled or interested in knowledge engineering. • We map both medical as well as technical domain expertise into
representations well suited to emerging capabilities of the semantic web. • We enable a mechanism to assess compliance with standards or
requirements within specific contexts for use.• We take a “toolbox” approach to statistical analysis. • We provide the capability in a manner which 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.
2525
QI-BenchStructure / Acknowledgements• Prime: BBMSC (Andrew Buckler, Gary Wernsing, Mike Sperling, Matt Ouellette)
• Co-Investigators– Kitware (Rick Avila, Patrick Reynolds, Julien Jomier, Mike Grauer)– Stanford (David Paik)
• Financial support as well as technical content: NIST (Mary Brady, Alden Dima, John Lu)
• Collaborators / Colleagues / Idea Contributors– Georgetown (Baris Suzek)– FDA (Nick Petrick, Marios Gavrielides) – UMD (Eliot Siegel, Joe Chen, Ganesh Saiprasad, Yelena Yesha)– Northwestern (Pat Mongkolwat)– UCLA (Grace Kim)– VUmc (Otto Hoekstra)
• Industry– Pharma: Novartis (Stefan Baumann), Merck (Richard Baumgartner)– Device/Software: Definiens, Median, Intio, GE, Siemens, Mevis, Claron Technologies, …
• Coordinating Programs– RSNA QIBA (e.g., Dan Sullivan, Binsheng Zhao)– Under consideration: CTMM TraIT (Andre Dekker, Jeroen Belien)
2626
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