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caBIG IVIWS SIG: Imaging Vocabularies

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caBIG IVIWS SIG: Imaging Vocabularies & Common Data Elements Breakout Overview Curtis P. Langlotz, MD, PhD University of Pennsylvania Daniel L. Rubin, MD, MS – PowerPoint PPT presentation

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Title: caBIG IVIWS SIG: Imaging Vocabularies


1
caBIG IVIWS SIGImaging Vocabularies Common
Data ElementsBreakout Overview
  • Curtis P. Langlotz, MD, PhD
  • University of Pennsylvania
  • Daniel L. Rubin, MD, MS
  • Stanford University
  • Mike Keller, PhD
  • Booz Allen Hamilton

2
NCI Informatics Long Range Planning, circa 1999
The CII
3
Importance of Common Data Collection Methods,
circa 1999
  • Serve as building blocks for the CII
  • Allow pooling of data and comparison of results
    among clinical trials
  • Facilitate enrollment of patients in clinical
    trials
  • Avoid redundant data collection (enter-once,
    use-many principle)
  • Automate and expedite administration of clinical
    trials

4
Medical Vocabularies Completeness for Radiology
Langlotz Caldwell, J Digit Imaging 15(1S)201,
2002
5
What is RadLex?
  • 26 participating organizations
  • 9 committees
  • 92 radiologist participants
  • 5,308 anatomic concepts

10-30 percent of these concepts are not found in
SNOMED-CT
6
Motivations for Common Imaging Terminology
  • Automatic indexing and retrieval of teaching
    files
  • Point and click structured reporting systems
  • Comparison or unification of disparate research
    databases
  • Reference datasets for cancer imaging research
  • Standardized image mark-up and annotation tools
  • Common vocabulary and data elements for cancer
    imaging

7
Fundamentals ofImaging Terminology Ontology
  • Daniel L. Rubin, MD, MS
  • Stanford University

8
Terminologies
  • A constrained list of terms
  • Usually shown as a list or taxonomy
  • Usually few attributes (e.g., ID code, synonyms)
  • Usually 1 or no relationships
  • No relations ? list of terms
  • 1 relation ? taxonomy
  • Use
  • Coding
  • Indexing
  • Simple search

------Diseases----- 003. _at_ OTHER SALMONELLA
INFECTIONS 003.0 SALMONELLA GASTROENTERITIS 003.
2 _at_ LOCALIZED SALMONELLA INFECTIONS 003.20
LOCALIZED SALMONELLA INFECTION,
UNSPECIFIED 003.21 SALMONELLA MENINGITIS 003.29
OTHER LOCALIZED SALMONELLA INFECTIONS ------Proced
ures----- 01. _at_ INCISION AND EXCISION OF SKULL,
BRAIN,... 01.0 _at_ CRANIAL PUNCTURE 01.01
CISTERNAL PUNCTURE 01.09 OTHER CRANIAL PUNCTURE
9
What is an ontology?
  • Similar to terminologies, specifying concepts
    (entities) and attributes
  • Also specifies multiple relationships among
    concepts
  • Permits rich knowledge representation
  • Supports complex inference
  • Use
  • Coding, indexing, and retrieval (like
    terminologies)
  • Reasoning and intelligent applications
  • Information integration
  • Semantic Web

10
Anatomy ontology explicit representation of
knowledge in various relationships
11
(No Transcript)
12
Vocabulary/CDE Strategy
Metadata storage formats
Metadata for Images
NLP
Terminologies CDEs
Queries Analysis
Image Annotation
Vocabularies Metadata
Formats Tools
Applications
13
Vocabulary/CDE Strategy
  • Metadata Terminology
  • Define image metadata useful to collect for
    cancer researchDevelop an image mark-up standard
    and associated open source and free annotation
    creation and display tools
  • Determine vocabularies ontologies to populate
    the metadata
  • Formats Tools
  • Define formats for associating data and metadata
    with images
  • Identify/develop tools for annotating images
  • Develop/reuse NLP methods to extract metadata
    from text
  • Testbed/applications using Vocabulary/CDE (tools
    and methods to use metadata to support cancer
    research)
  • Retrieve cases based on terminology-based queries
    and image annotations (e.g., trends in tumor
    size, image features)
  • Use ontology annotations on images to combine
    image data with clinical and molecular data

14
Vocabularies Common Data ElementsProposed Work
Items1 and 2
  • Curtis P. Langlotz, MD, PhD
  • University of Pennsylvania

15
Proposed Work Items
  1. Create caDSR compatible CDEs from standard
    imaging vocabulary terms
  2. Cancer imaging research playbook Devices,
    procedures, and protocols
  3. Using terminology/ontology to markup or annotate
    images
  4. Evaluate natural language processing (NLP) tools
    for prose image metadata (e.g. radiology reports)

16
ACRIN
  • American College of Radiology Imaging Network
  • NCI-funded imaging clinical trial cooperative
    group
  • Dozens of trials funded, including some very high
    profile trials (DMIST, NLST)
  • Tens of thousands of subjects
  • Case report forms containing hundreds of
    potential CDEs

17
Data Collection CDE Example
  • Please describe the margins of the mass
  • Smooth
  • Lobulated
  • Irregular
  • Spiculated
  • Obscured

18
Data Collection CDE Example
  • Please describe the margins of the mass
  • Smooth
  • Lobulated
  • Irregular
  • Spiculated
  • Obscured

19
The Playbook for Imaging in Cancer Research
  • Cancer Research Imaging Procedures and Protocols
  • An ontology of the imaging devices, procedures,
    and protocols that are used for experimental
    cancer imaging
  • (e.g., 7T 18-cm horizontal bore 4.7T 33-cm bore
    magnet operating at 200 MHz for 1-H imaging
    experiments)
  • Common, vendor-independent language to describe
    experimental imaging instruments.

20
Proposed Work Items
  1. Create caDSR compatible CDEs from standard
    imaging vocabulary terms
  2. Cancer imaging research playbook Devices,
    procedures, and protocols
  3. Using terminology/ontology to markup or annotate
    images
  4. Evaluate natural language processing (NLP) tools
    for prose image metadata (e.g. radiology reports)

21
Vocabularies Common Data Elements Proposed
Work Items3 and 4 and Summary
  • Daniel L. Rubin, MD, MS
  • Stanford University

22
Formats Tools
  • Metadata Storage Formats
  • Need to define a format to associate
    instantiations of metadata (annotations) with
    images
  • Image Annotation (mark-up)
  • Need tools to annotate images and that adopt
    metadata standards adopted by caBIG
  • NLP
  • Goal access free text to allow correlative
    research with images
  • Medium radiology/pathology reports published
    literature
  • Uses indexing/retrieval, information extraction

23
Metadata Terminology
  • Metadata
  • Determine requirements for metadata
  • Interview cancer researchers (NCI-funded
    Cooperative Clinical Trial Therapy Groups, ACRIN,
    industry) re image access/analysis needs
  • Review prior image-based cancer trials
  • Inventory other image metadata standards
    efforts
  • DICOM, HL7, Commercial systems
  • Consider analogy to MIAMI (microarray
    experiments)the minimal information necessary to
    describe a medical image
  • Identify PHI data fields to help other
    applications to anonymize data

24
Image Annotation
  • Inventory existing tools for annotating images
  • Create custom tools for associating metadata with
    images
  • Image annotation tool
  • Structured data acquisition tool that is part of
    clinical trial data collection process, or
    integrates with existing clinical trial tools

25
Natural Language Processing
  • Determine requirements for NLP
  • E.g., extract entities and relations from
    radiology reports map to ontologies, etc
  • Inventory existing NLP tools
  • caTIES, MEDLEE, Ricky Taira tools, Meta-Map and
    open source
  • Select or develop NLP tools to fulfill
    requirements

26
Overall Mission Motivating the Breakout Session
  • Extract meaning from imaging data to improve
    outcomes for patients with cancer or pre-cancer
  • Support correlative imaging science
  • Clinical trials are conducted by Cancer Centers,
    Consortia, and Cooperative Groups
  • Need to structure imaging content of such trials
  • Transmit the pertinent imaging data and metadata
    together with clinical trials data to an archive
    maintained by the NCI
  • Need query and data mining capability to
    determine trends and patterns in imaging data
    across clinical trials

27
Vocabulary/CDE Strategy
Metadata storage formats
Metadata for Images
NLP
Terminologies CDEs
Queries Analysis
Image Annotation
Vocabularies Metadata
Formats Tools
Applications
28
Vocabulary/CDE Strategy
  • Metadata Terminology
  • Define image metadata useful to collect for
    cancer researchDevelop an image mark-up standard
    and associated open source and free annotation
    creation and display tools
  • Determine vocabularies ontologies to populate
    the metadata
  • Formats Tools
  • Define formats for associating data and metadata
    with images
  • Identify/develop tools for annotating images
  • Develop/reuse NLP methods to extract metadata
    from text
  • Testbed/applications using Vocabulary/CDE (tools
    and methods to use metadata to support cancer
    research)
  • Retrieve cases based on terminology-based queries
    and image annotations (e.g., trends in tumor
    size, image features)
  • Use ontology annotations on images to combine
    image data with clinical and molecular data

29
Proposed Work Items
  1. Create caDSR compatible CDEs from standard
    imaging vocabulary terms
  2. Cancer imaging research playbook Devices,
    procedures, and protocols
  3. Using terminology/ontology to markup or annotate
    images
  4. Evaluate natural language processing (NLP) tools
    for prose image metadata (e.g. radiology reports)

30
  • The End
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