Standards to Enable Efficient Interpretation of Imaging Data · Standards to Enable Efficient...
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Standards to Enable Efficient
Interpretation of Imaging Data
ACR Center for Research and Innovation
1818 Market Street
Suite 1720
Philadelphia, PA
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Panelists
Lindsey Dymond, Director of Data Management [email protected]
Jim Gimpel, Imaging Core Lab Manager [email protected]
Charlie Apgar, Chief Operations Director [email protected]
Amanda Buck, Regulatory Specialist [email protected]
Adam Opanowski, Imaging Analyst [email protected]
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Staff and Infrastructure
American College of Radiology
37,000 MembersA
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Core Purpose: Serve patients and society by empowering members
to advance the practice, science, and professions of radiological care.
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The ACR Data Value Chain
Primary
Research
Developing
Guidance
• Data Collection
• Discovery
• Validation
• R&D
MonitoringEducation &
Training
• Expert
Consensus
• Algorithms
• Standards &
Guidelines
• Accreditation
• RadPeer
• Outcomes
Research
• Registries
• Quality Programs
• Ed Center
• Distributed
Education
Influence &
Adoption
• Publications
• Advocacy
• Economics
• Marketing
People, Process, and Technology Infrastructure
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The ACR Data Value Chain
Primary
Research
Developing
Guidance
• Data Collection
• Discovery
• Validation
• R&D
MonitoringEducation &
Training
• Expert
Consensus
• Algorithms
• Standards &
Guidelines
• Accreditation
• RadPeer
• Outcomes
Research
• Registries
• Quality Programs
• Ed Center
• Distributed
Education
Influence &
Adoption
• Publications
• Advocacy
• Economics
• Marketing
People, Process, and Technology Infrastructure
Research Focus: advancing the science of radiology…
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ACR Research and Innovation
Clinical Research
• Spans more than 40 years
• Over 500 clinical trials
• 2 million images processed annually
• Established research infrastructure in Philadelphia
16,318 square feet of office space
>130 researchers on staff
Distributed staff and technology
Remote and central interpretation and analysis environment
Passed FDA audits (not for cause) with no deficiencies
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Services• Study Design
• Protocol, Charter, Imaging Manuals
• Site selection and Qualification
• Training
• Integrated Diagnostics
• Study execution and operational management
• Statistical analysis
• Clinical Trials, Registries, other projects
• Big Data/Archives
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Evolution of ACR CRI
…as of 2014
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Since 2014…
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ACR Enterprise Solutions
Robust Data
Ecosystem
Research
Registries
Accreditation
• LCSR
• IR
• CTC
• DIR
35,000 sites
• NOPR
• IDEAS
• Screening/Detection • Novel Imaging Methods • Imaging Biomarkers • Socio-Economic Research
(Neiman Health Policy Research)
• Software/Hardware Validation
• GRID
• ICE
• NMD
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Object Store
QA ProcessesTRIAD
Central Processing
PACSSite Submission
Cloud-Based Analytic
Environment (e.g. Imaging
Pipeline)
Data Ecosystem: TRIAD and DART
Advanced Search
DART Portal
DataMapper
Authentication
Authorization
Data Provenance Portal DB
Users
Genomic DataBiospecimen Data
Clinical Data Image MetaData
Integrated Meta Data
DART Integrated Repository
Review Workflow
Image Interpretation & Reporting
Clinical Data / EDCClinical Data /
EDCEMR/ EDC
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Standards to Enable Efficient
Interpretation of Imaging DataSite
Selection
Image Acquisition
Image Collection
Image QC
Image Analysis
Assessment
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Standards: critical to interpretationLifecycle Step Key Considerations Recap standards and source of the standard
Site selection Qualified to perform the required imaging
Access to Resources (imaging platform, agents, etc)
Access to eligible subjects and referral relationships
Qualified researchers available Protocol, Imaging Charter, FDA Clinical Trial Imaging
Endpoint Process Standards (Guidance for Industry)
Site
qualification
Accreditation
Training
Test image submission
Image
acquisition
Platform specific for the study
Test submission
Protocol, Imaging charter, Imaging manual,
Manufacturer guidelines, QIBA profiles, FDA Clinical
Trial Imaging Endpoint Process Standards (Guidance
for Industry), 21CFR Part 11
Image
Collection
Electronic image transfer
Submission within defined standard of time
Protocol, Imaging Manual, FDA Clinical Trial Imaging
Endpoint Process Standards (Guidance for Industry),
21CFR Part 11, DICOM standards, Guidance for
Industry: Computerized Systems Used in Clinical
Investigations, Guidance for Industry: Electronic
Source Data in Clinical Investigation, QIN
Image QC Study-specific QC methods
Autocapture of data/DICOM header data
Image Analysis Qualitative vs. Quantitative
Timing requirements
Source of Analysis and collection of results
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Standards to Enable Efficient
Interpretation of Imaging Data
3 Areas for discussion
• Central Read
• Validated tools to analyze data
• Data access, transfer, and dissemination
Existing Standards:
Protocol, Imaging Charter, Imaging Manual, 21CFR Part 11, FDA Guidance for Industry: Clinical Trial Imaging Endpoint Process Standards, FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, FDA Guidance for Industry: Electronic Source Data in Clinical Investigation, QIBA, QIN
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(1) Central Read
Hypothesis: More efficient interpretation of
imaging data can be achieved through a
central read paradigm which supports remote
readers.
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Standards for Central ReadOperational
Considerations/Imaging
Charter
Access to images to read
Software and Hardware
Reader environment
Monitoring the readers
Integration of other data for
presentation
Capture of reader results
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Standards for Remote ReadCharacteristic Remote Reader Approach
Access to images to read Controlled access, validated
systems
Software and Hardware Hosted to ensure standardized;
specifications for viewing and
transfer of data
Reader environment Dictated by sponsor/imaging
manual
Monitoring the readers Deploy staff, monitor via
webcam, shadow operator
Integration of other data for
presentation
Selective presentation to ensure
unbiased read
Capture of reader results Direct data entry to EDC, auto-
capture of data
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(2) Validated Tools for Analysis
Hypothesis: Machine learning can lead to
increased efficiency in interpretation of
imaging data by expediting the process,
incorporating quantitative analysis tools, and
reduce human variability involved in the
process
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DEEP LEARNING OF MEDICAL IMAGESTRAINING PROCESS
Findings
No Findings Untrained Neural Network
Finding 1
Finding 2
Finding 3
Finding 4
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Finding 1
Finding 2
Finding 3
Finding 4Unknown
Trained Neural Network
DEEP LEARNING OF MEDICAL IMAGESAPPLICATION PROCESS
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AlgorithmZebra has developed an algorithm that
automatically calculates Coronary Calcium
Scores based on standard, non-contrast
Chest CTs. This tool can provide early
detection of people at high risk of severe
cardiovascular events.
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What future can we envision?
Define ROI • Algorithm
AIM
Launch QtvApplication
Generate output data
Incorporate into
database
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Standards for more validated tools
• Can we validate algorithms/methods developed by the
community?
• What types of studies/analyses could benefit from
such capabilities?
• Is there a potential impact on reader study designs?
Could sufficient variability be eliminated to adopt
single reader model?
how do we ensure that such algorithms
will be accepted by FDA?
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(3) Data Access and Transfer
Hypothesis: Data warehouses can create a
more efficient platform for interpretation of
imaging data for analysis, transfer, and
dissemination
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Data access and dissemination
standards• Analysis
• Retrospective central read, or
• Concurrent access and reading?
• Transfer to Sponsor/FDA
• File transfer, or
• Access to reports and integrated source data?
• Future Research
• Proprietary or a community good?
• Project Data Sphere
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Project Data Sphere
• Project Data Sphere, LLC (PDS), an independent, not-for-profit initiative of the CEO Roundtable on Cancer's Life Sciences Consortium (LSC), operates the Project Data Sphere platform, a free digital library-laboratory that provides one place where the research community can broadly share, integrate and analyze historical, patient-level data from academic and industry phase III cancer clinical trials.
• The Project Data Sphere platform is available to researchers affiliated with life science companies, hospitals and institutions, as well as independent researchers. Anyone interested in cancer research can apply to become an authorized user.
• A goal of the Project Data Sphere initiative is to spark innovation. The Project Data Sphere initiative can help the cancer community unlock the potential of valuable data by generating new insights and opening up a new world of research possibilities.
• The true power of this platform will come from an engaged, diverse and global community focused on advancing future research to improve the lives of cancer patients and their families around the world.
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Our Model….
DART Data Store
AIM Module
AIM
Template(CDE Compliant)
Dart Imaging
Pipeline DART
Analytics
Conduct
Image
Review and
Markup
Test Hypotheses
based on
integrated Clinical,
Imaging, and Other
Data Sets
AIM Markup
Stored in
DART
Load AIM
TemplateIdentify
Hypotheses to
test on new and
existing Data
Acquire Images and
Data According to
Protocol
Upload, QC and
Process Imaging
Data
1
23
6
7
8
10
Markup and Secondary
Analysis
RAVETRIAD Populates
RAVE DB and
Forms
4
TRIAD to DART
Imaging Data, RAVE
to DART Clinical Data
5
RaveRave Integrate
Pathology, Omics,
and other Clinical
Data as available
9