# Presentation: 182293

## caBIGTM Imaging Workspace

*SIG Breakout Report for:*
*Vocabularies & Common Data Elements SIG*

## Possible Near-term SIG Software Projects

- Evaluations of suggested topics?
- Propose support for incorporation of ACRIN CDEs into caDSR along with appropriate terminology into NCI Thesaurus (two separate parts of same effort)
- Propose support of creation of Cancer imaging research “playbook”-generic well defined terms for imaging devices, procedures and protocols (DICOM, RadLex Clinical Playbook, ACRIN)
- Propose metadata for annotation of image (much like MIAME for microarray)
- Proof of Concept-MIAME for RIDER
- Unit of analysis:  lesion?  Image?  Study?
- Not the results-data to describe methodology
- Propose technology evaluation of caTIES, MEDLEE, Ricky Taira tools, Meta-Map and open source
- Annotation of Images (software-related to #3)
- Structured Data Capture (software-related to #3)

## Possible Near-term SIG Software Projects

- Creation of ontology for data collection of imaging clinical trials using RadLex
- Propose support for incorporation of ACRIN data collection questions into caDSR
- RadLex/NCI Thesaurus (EVS) semantic analysis
- RadLex/DICOM
- ACRIN data collection questions, large # of use cases, based on those create ontology to organize
- Reach out to clinical trials that use imaging (industry) to identify additional data collection questions that can be used as use cases
- Go through ACRIN data collection questions to determine gap between ACRIN elements and data elements already in caDSR This is the gap analysis)
- If yes, we are done
- If no, go to next step
- Based on gap analysis, generate prioritized list of data collection questions to load to caDSR
- May have broader application in caBIG

## Possible Near-term SIG Software Projects

- DICOM into EVS and caDSR
- Approximately 97 terms in NCI Thesaurus now (get list of DICOM tags to Imaging VCDE group)
- Working Group 18 of DICOM relevance?
- Attaching CT data to DICOM file
- May be relevant to animals

## Possible Near-term SIG Software Projects

- Cancer Imaging Devices, Procedures and Protocols
- Generic language describing Image acquisition
- Harmonize with existing DICOM and RadLex fields
- Generic language describing pre-processing (before interpretation)
- Vendor specific algorithms
- Generic language describing post-processing (before interpretation)
- Take from DICOM
- Next Steps
- Extract catalogue items from DICOM
- DICOM contains all modalities
- Terms to describe CT, MR, PET
- RadLex/DICOM
- Engage Modality Societies
- Provide review of “Playbook”

## Image Annotation

- Cavitary mass

## Structured data capture

- In the left lower lobe, there is a mass...

**Margins: **lobulated
**Length:** 2.3cm
**Width: **1.2cm
**Cavitary:** Y
**Calcified:** N
**Spatial relationships:** Abuts pleural surface; invades aorta

## Possible Near-term SIG Software Projects

- Image Annotation
- Identify list of potential annotation tools to be used for images
- Hyperlinks to coordinates in image from report text (both directions)
- Design
- Scope
- Use Cases
- ACRIN
- Reach out to oncologists
- Clinical needs-staging
- Integration with clinical workflow (speech recognition)
- Longitudinal analysis (eg tumor response over time)
- Requirements
- Software architecture
- Use of IHE transactions and grid components
- Storage architecture

## Possible Near-term SIG Software Projects

- Natural Language Processing
- Unstructured information will always exist
- Should be use case driven
- Focused extraction from radiology report
- Findings
- Nodules and their descriptors
- Anatomy
- Change in findings over time
- Population of templates
- Use of extractions
- Automated population of metadata
- Decision support
- Data mining
- Automated comparison
- Automated population of clinical trials database
- Screen for eligibility for clinical trials

## Possible Near-term SIG Software Projects

- Natural Language Processing
- Next Steps
- Propose cancer specific technology assessment of:
- MEDLEE (radiology reports)
- Ricky Taira tools (radiology reports)
- Select platform for adaptation of radiology reports as needed
- caTIES
- Meta-map
- GATE
- Test versus human concept extraction

## Demonstration Straw Man

- Meta data assigned to images in research image repositories (LIDC, RIDER, NLST)
- Structured data captured about nodules/polyps at the time of primary reading (lung, prostate, colon)
- Historical information about patient extracted from text reports
- Multiple imaging modalities
- Linkage to genotype information using clinical ontology harmonized to gene ontology
- Retrieval of images across image databases using metadata, annotation data, structured capture data
- “Show me all of the <2cm lobulated nodules in the left lower lobe”
- “Show me rate of tumor growth after drug treatment”
- Linked to data from medical record of patient

## Example Near Term Project Interest

| Project | Short Term Win | Critical to Demonstration | Priority |
| --- | --- | --- | --- |
| Creation of ontology for data collection
of imaging clinical trials using RadLex | yes | yes | 1 |
| ACRIN data elements into caDSR  | no | no | 2 |
| DICOM to caDSR/EVS with evaluation | yes | yes | 1 |
| Cancer imaging devices, procedures and protocols | no | no | 3 (dependent on 3) |
| Meta data; Image Annotation; structured data capture  | yes | YES (dependent on 1, 3) | 1 |
| Natural Language Processing-Evaluation | yes | no | 1 |
| Natural Language Processing-Development | no | no | 3 |