# Presentation: 193752

## caTIES Processing Text to Share Data and Tissue

- Rebecca Crowley, Kevin Mitchell, and Wendy Chapman
- Centers for Oncology and Pathology Informatics
- Center for Biomedical Informatics
- _[crowleyrs@upmc.edu](mailto:crowleyrs@upmc.edu)_
- June 4, 2006

## caTIES project goals

- Support research (clinical, translational, basic) that uses human tissue or tissue related data by providing de-identified, concept-based access to surgical pathology report data associated with tissue slides and blocks.
- caTIES v2.0 now in place at four institutions: University of Pittsburgh, University of Pennsylvania, Thomas Jefferson University, University of Washington St. Louis

## Potential Uses

**FINAL DIAGNOSIS:**
**PART 1:     LYMPH NODES, LEFT PELVIC, LYMPHADENECTOMY **
**                   ****SIX BENIGN LYMPH NODES WITH NO EVIDENCE OF MALIGNANCY.**
**PART 2:     LYMPH NODES, RIGHT PELVIC, LYMPHADENECTOMY **
**                   ****TWO BENIGN LYMPH NODES WITH NO EVIDENCE OF MALIGNANCY.**
**PART 3:     PROSTATE AND SEMINAL VESICLES, RADICAL PROSTATECTOMY **
**INVASIVE MODERATELY DIFFERENTIATED PROSTATIC  ADENOCARCINOMA WITH A COMBINED GLEASON SCORE OF 3+3=6.**
**PATHOLOGIC STAGE T2bN0MX**
** **

-  Case finding for paraffin materials
-  Automated annotation for caTISSUE CAE
-  Text-mining
-  Automation of clinical trials eligibility screening
-  Cancer Surveillance

## 
Small (hundreds to thousands of specimens)
Annotated with human abstraction & manual data entry
New (less than 10 years old)
Focused
Mainly Frozen Tissue
Storage QA/QC
RNA studies

- Small (hundreds to thousands of specimens)
- Annotated with human abstraction & manual data entry
- New (less than 10 years old)
- Focused
- Mainly Frozen Tissue
- Storage QA/QC
- RNA studies

- The tissue bank is....

## Large (Millions of Specimens)
Directly Documented by Pathology
Old (Decades)
Ubiquitous & General
DNA and Protein Studies
TMA
“Rare and Discontinued Tumors”
“Tissue Across Generations”
Fairly Intact

- Directly Documented by Pathology
- Old (Decades)
- Ubiquitous & General
- DNA and Protein Studies
- TMA
- “Rare and Discontinued Tumors”
- “Tissue Across Generations”
- Fairly Intact

- The paraffin (clinical) archive is...

## Problems to solve

- Reduce unnecessary barriers and unnecessary work
- Make entire corpus de-identified and searchable
- Improve retrieval
- Many ways to say the same thing (synonymy)
- Pertinent negatives (negation)
- Discourse inferences
- Information Extraction
- Temporality and aggregation
- Create network of institutions sharing data; building collaborations
- Interoperable with other systems

## Long Term Project Goals

- Develop software with value to individual cancer centers as well as caBIG matrixed researchers
- Use caTIES as a platform for research - improve our ability to extract meaningful information from free-text pathology reports

## Current caTIES functionality

- 1. Secure communication between participating organizations coordinated by the Open Grid Service Architecture (OGSA) Services for data transfer and de-identification
- 2. Concept-coding of surgical pathology reports using a GATE-based NLP pipeline
- 3. Databases for private and sharable information accessible via OGSA-Data Access and Integration services. OGSA-DAI is an extension of OGSA.
- 4. A graphical user interface for concept-based querying across the network, role-based administration of access, and protocol-based ordering of tissue samples
- 5. A set of semantic annotations and services based on caCORE and caGRID which foster semantic interoperability between caTIES and other caBIG applications.

## Problems to solve

- Reduce unnecessary barriers and unnecessary work
- Make entire corpus de-identified and searchable
- Improve retrieval
- Many ways to say the same thing (synonymy)
- Pertinent negatives (negation)
- Discourse inferences
- Information Extraction
- Temporality and aggregation
- Create network of institutions sharing data; building collaborations
- Interoperable with other systems

## Before HIPAA

## After HIPAA

## What we hope will happen with caTIES

## Problems to solve

- Reduce unnecessary barriers and unnecessary work
- Make entire corpus de-identified and searchable
- Improve retrieval
- Many ways to say the same thing (synonymy)
- Pertinent negatives (negation)
- Temporality and aggregation
- Create network of institutions sharing data; building collaborations
- Interoperable with other systems

## Slide 14

## Text Acquisition

- Loads the ‘Identified’ caTIES MySQL datastore
- Minimize manual processes
- Text acquisition service for bulk loads
- Chunker for text reports
- HL7 acquisition service at Penn
- Cerner/CoPath caBIG Data Extractor allows scheduled batch input from Cerner CoPath
- Would like to get other LIS Adapters

## De-Identification Service

- caTIES De-Identification service scrubs pathology report, creates de-identified identifiers, loads ‘De-Identified’ caTIES datastore
- caTIES de-identification service wraps the de-IDTM software (Saul, Cooper, etc)
- Safe-Harbor method removes HIPAA mandated identifiers
- Creates tokens for names and preserves temporal relationships
- De-ID works with adopters as each site comes on-line

## De-Identification Service

- De-Identification Service

## De-ID service creates linkage through key pair on identified side of the system
‘Honest Broker’ view of data behind the firewall allows only HB to see identifiers in order to fill requests for tissue

- ‘Honest Broker’ view of data behind the firewall allows only HB to see identifiers in order to fill requests for tissue

- Preserving identifying relationship

## Problems to solve

- Reduce unnecessary barriers and unnecessary work
- Make entire corpus de-identified and searchable
- Improve retrieval
- Many ways to say the same thing (synonymy)
- Pertinent negatives (negation)
- Temporality and aggregation
- Create network of institutions sharing data; building collaborations
- Interoperable with other systems

## Pipeline Service

## Current Processing Resources

- (1)   Resetter – clears document, deletes existing annotations,
- (2)   Tokeniser - tokenizes words, numbers, punctuation and spaces,
- (3)   Chunker - parses reports into sections, parts, sentences, and phrases,
- (4)   Spell-checker - identifies erroneous spelling and suggests frequency based correction
- (5)   RegEx - annotates a pre-defined set of attribute and value pairs such as tumor grade and stage,
- (6)   Vocabulary Concept Tagger - annotates fragments of free text to associated concept from controlled terminology
- (7)   Semantic-type Filter - removes concepts associated with unwanted semantic types,
- (8)   NegEx - implements NegEx negation detection algorithm to tag explicitly negated concepts,
- (9) Semantic-type Categorization - extracts organs, procedures, diseases; infers topology of concepts as modifiers of other concepts

## Problems to solve

- Reduce unnecessary barriers and unnecessary work
- Make entire corpus de-identified and searchable
- Improve retrieval
- Many ways to say the same thing (synonymy)
- Pertinent negatives (negation)
- Temporality and aggregation
- Create network of institutions sharing data; building collaborations
- Interoperable with other systems

## Technology

- Decentralized data management and centralized order management
- Grid services
- Security

## Decentralized Data – Centralized Ordering

- Although all report-derived data are decentralized, we need a centralized way to manage orders

## Grid services for sharing data

- Globus toolkit
- We are in some ways purposely creating two systems, one of which is isolated from the caGrid
- Need for Virtual Organizations – not everyone is going to want to share their data with everyone else!

## Security

- Physical and network
- Application-level
- Grid-level
- Processes and Policies

## Processes and Policies

- HIPAA
- Institutional Review Boards
- Security Compliance
- Trust agreements
- Honest Brokers
- Materials transfer agreements for tissue
- caTIES is testbed for new Security Policy

## Problems to solve

- Reduce unnecessary barriers and unnecessary work
- Make entire corpus de-identified and searchable
- Improve retrieval
- Many ways to say the same thing (synonymy)
- Pertinent negatives (negation)
- Temporality and aggregation
- Create network of institutions sharing data; building collaborations
- Interoperable with other systems

## Definition

- Semantic interoperability is the ability of information systems to exchange information on the basis of shared, pre-established and negotiated meanings of concepts, such that the input received can be machine processed

## Acknowledgements

- Kevin Mitchell
- Girish Chavan
- Adi Nemlekar,
- Linda Schmandt
- Eugene Tseytlin
- Dean Brown, Rick Nestler, Zeke Holland (coPath) - Acquisition
- Melissa Saul, Paul Hanbury, Greg Cooper – DeID
- Steve Merahn – DeID
- Wendy Chapman – NegEx
- Mike Becich, John Gilbertson, Jim Harrison
- University of Pennsylvania (Mike Feldman, Dave Fenstermacher, Tara McSherry)
- Washington University and Thomas Jefferson University
- GATE, UMLS Lexical Tools, NCI Enterprise Vocabulary Services
- Funding from the Shared Pathology Informatics Network (NCI) and caBIG (NCI)