Pathology and Oncology Informatics Opportunities and Threats APIII 2005, Lake Tahoe, CA Friday, Augu

1
Pathology and Oncology InformaticsOpportunities
and Threats APIII 2005, Lake Tahoe, CA Friday,
August 26th, 2005

• Michael J. Becich, MD PhD (becich_at_pitt.edu)
• Vice Chairman of Pathology,
• Professor of Pathology, Info Sciences
  Telecommunications
• University of Pittsburgh School Medicine
• Director, Center for Pathology Informatics
• http//path.upmc.edu/cpi
• Course Co-Director, Advancing Practice,
  Instruction and
• Innovation through Informatics
  (http//apiii.upmc.edu)
• Ad Hoc Councilor, Association for Pathology
  Informatics
• http//www.pathologyinformatics.org

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Disclosures by MJB Team

• Corporate Support for API and APIII
• 650K projected for 2005 Cerner, Misys, IMPAC CAP
  Today, Cisco, Verizon, AACI, Aperio, Apollo,
  Applied Bioinformatics, Ardais, Bayer Healthcare,
  Beckman Coulter, Bioimagine, DakoCytomation,
  Chromavision, GE/Amersham Biosciences, General
  Data, Humintec, InterScope Technologies, Nikon,
  Olympus, PSA, Psyche, Roche Diagnostics,
  ScanSoft, SCC Soft Computing, Synchroscopy,
  SNOMED, Taylor Data, Thermo Shandon, Trestle and
  Voicebrook and others
• Corporate Sponsored Research Agreements
• 3.0M in 2005 Amgen, Ardais Corp, Aurora
  Interactive, IBM, Cerner, Misys, Nikon, Olympus,
  Pittsburgh Life Sciences Green House, Sequel
  Genetics Trestle Corp.
• Startup/Public Companies (Founder, Equity
  Consultant)
• InterScope Technologies, Inc. -
  http//www.interscopetech.com
• Member of the Board of Directors, Paid Consultant
• Provider of high speed/volume whole slide
  imaging/telepathology systems
• Ultrarapid whole slide imaging Gb data
  transfers, terabyte storage and robotics
• Icoria (NASDAQ ICOR) formerly Paradigm Genetics
  merged with TissueInformatics, Inc., 03/04,
  http//www.icoria.com
• Systems biology as a CRO to pharma, agra and
  biotech venture backed TVM, Motorola.
• Hyperquantitative image analysis, genomics and
  bioinformatics capabilities.
• Consultancies (in addition to Trestle Corp.)
• Pathology Education Consortium (PEC) with Bruce
  Friedman (volunteer)
• Misys Strategic Planning Group and Physician
  Advisory Board (paid)
• ThermoElectron Physician Advisory Board (paid)

3
Outline

• SWOT Analysis for Pathology Informatics
• Strengths Imaging and Tissue Banking
• Strengths - 70 of data in EMR/70 of medical
  decisions 3 budget
• Strengths - recycle opportunities tissue
  banking
• Weaknesses Lack of imaging and information
  standards
• Weaknesses Pathologists lack of partnership
  with Radiology
• Weaknesses Fear of change from traditional
  practice roles
• Weaknesses Decreasing practice margins, poor
  funding base (except maybe for tissue banking)
• Opportunities Digital pathology and Tissue
  Banking
• Opportunities - Pathologist as data miner and
  role in bioinformatics
• Threats EMR Radiology
• Threats Lack of Support for Molecular
  Diagnostics, Emerging Tech
• Threats Lack of integration between AP and CP
• Threats Significant under-investment in
  Training
• Conclusions

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Outline

• SWOT Analysis for Oncology Informatics
• Strengths Strong funding base, e.g. clinical
  trials and biomarkers
• Strengths - Very common disease hence
  philanthropy is everywhere
• Strengths 70 of pathology work is oncology
  related
• Weaknesses Lack of solid information systems
  (commercial)
• Weaknesses Lack of integration w/ Radiation Onc
  (60 OP visits)
• Weaknesses Fear of change from traditional
  practice roles
• Opportunities caBIG and NIH Roadmap Initiatives
• Opportunities Multispecialty nature touches all
  of medicine (Path, Med, Surg, Radiology,
  Radiation Onc, Pharmacy, etc)
• Threats Lack of a tailored EMR (except 1
  company)
• Threats Funding cap after run up
• Threats Reimbursement in Med Onc under siege
  pressures informatics investment
• Threats Lack of investment in training
  (dramatic)
• Conclusions

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SWOT Analysis for Pathology

• Strengths
• Imaging has matured and whole slide imaging is a
  reality hence digital pathology is emerging
  look at radiology today!!!
• American Board of Pathology whole slides for AP
  boards
• CAP Virtual Slides Seminars
• US Labs Virtual Image Analysis
• Trestle (formerly InterScope) Has a complete
  workflow design with HL7 messaging and API to
  allow LIS interact with imaging context and
  content
• Web enabling pathology reporting
• Telepathology as model systems for Reporting
• Enhanced Pathology Reporting is being used as a
  way to compete for laboratory testing

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SWOT Analysis for Pathology

• Weaknesses
• Lack of Imaging and Information Standards
• Need to promote imaging standards
• HL7 RIM (version 3) provides an object model
  for true information transport
• SNOMED need a reference terminology tower of
  Babel
• CAP Cancer Checklists now a requirement for
  ACOS certification
• Synoptic reporting too slow for uniform
  adoption and integration
• Fear of Loss of Current Comforts
• Pathologists need to think forward to their
  future roles as information mangers and knowledge
  engineers instead of keeping in the comfort
  zone as diagnostician only This is a critical
  problem today.
• Pathology leadership need to really come to
  understand how the practice is evolving
  Embracing Practice Innovation is KEY!!!

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SWOT Analysis for Pathology

• Opportunities
• Digital pathology (concentrating on Advancing
  Practice only)
• Quality assurance true peer review, perhaps
  by experts
• Second opinions on all new diagnoses of cancer,
  metabolic diseases and rejection in
  transplant/cellular therapies
• Access to archival images key to management of
  transplant and cellular therapies
• A platform for accomplishing
• Quantitative Analysis for Immunohistochemistry
• Content Based Image Retrieval check your
  diagnosis against a collaborative telemicroscopy
  system
• Advance new imaging methodologies to innovate
  pathologist role
• Proteomic based analysis of whole sections
  (Vanderbilt)
• Move to eliminate errors in pathology diagnostics
• Pathologist as data miner
• Evolution from data manager to knowledge
  engineer/data miner
• Less time spent on generating the test result and
  more time integrating it with other data sources

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SWOT Analysis for Pathology

• Threats (concentrating on Advancing Practice
  only)
• Electronic Medical Record Systems (EMR)
• We are becoming lazy in Pathology and our
  outsourcing the delivery of lab data to the EMR
• Need to re-up our commitment to be the curator of
  lab data and its representation to the patients,
  doctors, specialists and health care
  professionals we server
• Radiology and PACS (Picture ArChiving Storage)
• There is intense concentration on the micron to
  millimeter space
• The big radiology vendors (digital radiology and
  PACS) are starting to claim and own the
  diagnostic imaging space
• The distinction between radiology based cellular
  imaging and pathology diagnostics at the sub
  millimeter level is blurring
• Pathologist must get involved in diagnostic
  imaging initiatives
• Lack of Integration Between AP and CP
• We are the biggest problems
• Need to think about integrated patient disease
  centric reports

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Pathology and Oncology SWOT AnalysisConclusions

• MJBs 5 rules to building a stronger future for
  Pathology Oncology
• Try to become a better listener.
• Ask the stupid questions you are afraid to ask
  and remember you are always a student and have a
  lot to learn, be curious!!!
• Realize that change is your friend even when it
  rattles your bones and shakes your confidence.
• Learn to publish and only do this in open source
  journals
• Share everything you produce through websites,
  data sharing sites and NIH Roadmap Initiatives.

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Informatics Challenges

• Introduction to AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

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Pathology Informatics Opportunities and Challenges

• Many test-ordering physicians are already facing
  information overload number of patients seen
  also ratcheting upwards in the age of
  personalized medicine
• Genomics/proteomics testing will exacerbate this
  problem due to volume complexity of info
  generated
• Information can no longer be presented solely as
  numbers (CP) text (AP) must now begin to
  present images, diagrams, schematics or we will
  lose to EMRs
• Classic LISs EMRs will be inadequate vehicles
  for displaying complex info generated by new
  pathology
• We must also integrate our information into the
  handheld and wireless environment to better serve
  our clients

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The AP/CP Integration Issue

• Where does The New Pathology lead to
• Look at trends over the few four decades
• Increasingly specialty laboratories reporting
  needs are not being met
• Tissue Typing
• Hematopathology
• Cytogenetics
• Microbiology
• Immunopathology
• Molecular Pathology
• Harder to distinguish which are CP
  (quantitative/numeric result driven) and which
  are AP-like (qualitative/text driven)
• Vendors have largely not engineered this into
  their solutions
• Problem is that AP- CP-LIS are not integrated.
• This is a basic flaw in pathology.

From Gilbertson and Becich, Adv Lab Mgr, 1998
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Pathology Informatics Opportunities and Challenges

• CP data is already fully integrated into the CPR
  using HL-7 and in rare instances (why?) LOINC
  these changes have been driven by demands from
  clinicians
• AP data is largely ignored by EMR/CPR initiatives
  (cell in a chart approach)
• Biggest Challengemeeting needs of Molecular
  Pathology
• Structured data entry in surgical pathology
  results ins uncompensated greater work burden
  for labs
• However, integrated, retrievable, longitudinal
  clinical data provides new opportunities and new
  product lines for labs/pathologists
• Next challenge is synoptic reporting and image
  capture and integration on the AP side
• Fundable opportunities on the research side of
  pathology - NCI and NIH Roadmap

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Informatics Challenges

• Introduction to AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

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Informatics Challenges in Molecular Pathology (MP)

• Primary challenge is lack of a information
  systems to meet the diverse needs of MP
• Molecular Hematopath as a model for The New
  Pathology
• Informal Survey of Audience about LIS (Lab Info
  Systems)
• Who uses their CP LIS for Molecular Pathology
  Reporting?
• Who uses their AP LIS for Molecular Pathology
  Reporting?
• Who is satisfied with their reports?
• Secondary Challenge is results reporting in the
  Electronic Medical Record (EMR) or Clinical Data
  Repository (more on this later)
• Secondary Challenge is also supporting test
  development in a LIMS systems (later as well)

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The AP vs. CP issue

• Where does The New Pathology lead to
• Molecular Hematopathology as the model
• Need to support bone marrow core and smear data
  with CBCs
• Support lymph node reports with surface marker
  studies
• Needs to integrate CP data into complex textual
  reports
• Need to integrate Molecular Hematopathology data
• Support microarray data (already at UCSD
  Tillman)
• Problem is that LIS are not integrated and
  neither are our AP can CP divisions in our
  Departments
• Molecular Pathology is the New Pathology
• By working with Informatics we can surmount this
  integration challenge by best practices
• Pathology Info Molecular Path need to join
  forces!!!

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Where are New Technologies Leading Us?

• On a pathway to integrate microarray technology
  into our clinical practice
• This will result in more complex data management
  as well as analytical needs
• Examples of this are in the leukemia and
  pediatric tumor space already
• Work by Staudt Leukemias
• Work by Triche Pediatric bone/soft tissue
  tumors
• AMP Molecular Classification of Lymphomas
• This requires new capabilities not currently
  supported by our LIS systems
• Need for true information systems for this work

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Why does Molecular Pathology require a LIS as
well as a LIMS?

• Laboratory Information System or LIS
• Addresses customized (standard) workflow and
  billing
• AP CP systems NOT integrated, grew from billing
• Historical evolution focused on managing single
  values (CP) and text reports (AP)
• Hybrid systems that efficiently handle
  quantitative (CP) and qualitative/descriptive
  data (AP) are not available
• Laboratory Info Management Systems or LIMS
• Historically used in pharma and biotech for test
  development and validation in research
• Not focused on billing but information management
• Allow for data mining and built to support data
  warehouse LIS does not allow this!!!

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Role of Tissue Banks in Molecular Testing
Research

• Providing banked controls for disease and
  normal are going to be increasingly critical for
  Molecular Pathology
• Patients own progression control to provide a
  backdrop for individualized therapeutic
  monitoring is critical in protein, gene and
  cellular therapies
• Data management and annotation is critical
  value-add to tissue banking feature Cancer
  Biomedical Informatics Grid (caBIG, see
  http//cabig.nci.nih.gov) - open source tool
  development as part of NIH Roadmap (more later)
• Development/maintenance of longitudinal
  databases, with images are critical often
  neglected-whole slide imaging
• Tissue banking also provides opportunity for
  Molecular Pathology labs to develop relationship
  with pharma, biotech and nanotechnology research

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Critical Roles of Tissue Banks and Informatics in
Molecular Pathology

• Molecular Pathology
• Biomarker Diagnostics prime location for marker
  development
• Genomics DNA and RNA expression microarrays
  SNPs
• Proteomics mass spectroscopy robotic 2D gel
  sampling
• Epigenomics methylation and other studies
• Tissue Banking
• Tumor Banks needed for all of above studies and
  clinical controls
• Serum and Genomic DNA for proteomic
  epigenomic studies
• Normal Tissue Banking needed for true controls
  both clinical marker development and research
• Warm Autopsy Banking needed for rare
  metastatic samples not obtained clinically
• Informatics
• Clinical Outcomes Infoneeded for translational
  basic research
• De-Identified Data research annotation of
  tissues banked
• Bioinformatics needed for marker development
  and analysis

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Shared Pathology Informatics Network De-identifica
tion and Autocoding
Record de-identified by de-ID v. 3.3PATIENT
HISTORYThe patient is a AGE-year-old male
with a clinical history of prostate cancer.OSS
PATH-NUMBERlt1gt, DATElt11/12/00gt, PLACE
.________________________________________________
_______________FINAL DIAGNOSISveiw
conceptsPART 1 PROSTATE, LEFT LOBE, NEEDLE
BIOPSY (OSS PATH-NUMBERlt1gt, DATElt11/12/00gt,
PLACE) A. MODERATELY DIFFERENTIATED PROSTATE
ADENOCARCINOMA. GLEASON' S PATTERN 33, SCORE
6, INVOLVING ONE OF FIVE CORES, LESS THAN 2 OF
TISSUE SUBMITTED (see comment).B. NO PERINEURAL
INVASION SEEN.veiw conceptsPART 2 PROSTATE,
RIGHT LOBE, NEEDLE BIOPSY (OSS PATH-NUMBERlt1gt,
DATElt11/12/00gt, PLACE) A. MODERATELY
DIFFERENTIATED PROSTATE ADENOCARCINOMA, GLEASON'
S PATTERN 34, SCORE 7, INVOLVING ONE OF FIVE
CORES, LESS THAN 5 OF TISSUE SUBMITTED.B. NO
PERINEURAL INVASION SEEN.kmrINITIALS kmr
NAME, M.D. Fellow/Chief Resident NAME, MBBS.
M.D.Resident NAME, M.D. NAME MMM VVV
NAME, M.D.DATElt0/2/00gt 1027_________________
______________________________________________OUT
SIDE ACCESSION 2 SLIDES LABELED
PATH-NUMBERlt1gtOUTSIDE NAME RECEIVED
YCONSULT MATERIAL DESCRIPTIONReceived for
consultation from NAME, M.D., are two (2)
consult slideslabeled PATH-NUMBERlt1gt from
PLACE , ADDRESS ,
22
Informatics Challenges

• Introduction to AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

23
Molecular Pathology and the Electronic Medical
Record

• Turf challenges with the paper record were
  limited tab in chart market lab and hardcopy
  reports
• Situation now more fluid with the EMR pathology
  information managers may need to fight for
  pathologys electronic turf especially in the
  case of Molecular Path
• Molecular Pathologys special needs are not
  represented in current vendor products (in fact
  much the same for all)
• All lab data should flow through the Pathology
  Department validation, integration, and
  formatting
• Not happening due to EMR control outside of
  Pathology
• This is not just an academic concern as central
  lab control of data dis-integrates, new data
  squabbles can be anticipated

24
Informatics Challenges

• Introduction to AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

25
Critical Role of Training

• API, APIII and Lab InfoTech Summit have provided
  a rich palate of educational opportunities
• Not nearly enough formal trainees in Pathology
  Informatics nor hybrid trainees in Molecular
  Pathology and Informatics
• Two trainees with this phenotype have been
  successful
• Need many more and collaboration between AMP and
  API
• CAP Foundation has a Informatics Fellowship
  program
• Molecular Genetic Pathology Fellowship program
• Are there any informatics requirement?
• Multiple Issues - We need to work on this
  together
• Abundant translational basic research opps

26
Informatics Challenges

• Introduction to AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

27
Informatics, Molecular Pathology the NIH
Roadmap
caBIG
cabig.nci.nih.gov
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caBIG Molecular Pathology Informatics Support

• Microarray Repositories
• caArray annotation of microarray data, for data
  sharing and integration (Directors Challenge
  data)
• Genome Annotation
• GeneMiner genome ontology miner
• Proteomics Informatics (LIMS tools)
• Pathway Tools (tie genome and proteome)
• Translational Tools (enable development)
• Data Analysis and Statistical Tools
• caWorkBench analysis tools for data exploration

29
National Biospecimen Network (NBN) Initiative

• NCI and C-Change (formerly National Dialogue on
  Cancer) initiative in partnership with Industry
• Goal Provide tissues with high degree of
  clinical annotation as well as analysis center
  approach to additionally providing omic data
  sets
• Not for profit activity with national
  distribution mechanism/governance
• Based on Best Practices in Tissue Bank report
  by Rand Corporation
• University of Pittsburgh and PCABC profiled as
  best practice in academia
• For full report see http//www.rand.org/publicatio
  ns/MG/MG120/
• Blueprint for NBN publicly available at
  http//www.ndoc.org/about_ndc/reports/pdfs/FINAL_N
  BN_Blueprint.pdf
• Pilot program initiated with Prostate SPOREs
• Deputy Director (Anna Barker) sill interested in
  a community based tissue banking pilot (hence
  discussion that follows)

30
Informatics Challenges

• Introduction to AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

31
Conclusions

• Molecular Pathology Pathology Informatics have
  fertile ground for collaboration to solve the
  AP/CP integration issues
• Both are ASIP divisions with growing membership
• Both are new, innovative, attractive areas of
  Pathology
• Common problems in search of solutions LIS
  LIMS
• To solve the AP CP schism, partnership is key
• Training is at a critical nexus for both
  organizations
• Need support from Pathology Leadership
• Continue to leverage the funding from NIH Roadmap
  and NCI to move forward

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Pathology Informatics Publications NOTE
Please e-mail me at becich_at_pitt.edu if you want
PDFs

• Yagi Y, Ahmed I, Gross W, Becich MJ, Demetris AJ,
  Wells A, Wiley CA, Michalopoulos GK, Yousem SA,
  Barnes B, Gilbertson JR. Webcasting pathology
  department conferences in a geographically
  distributed medical center. Hum Pathol. 2004
  Jul35(7)790-7.
• Zheng L, Wetzel AW, Gilbertson J, Becich MJ.
  Design and analysis of a content-based pathology
  image retrieval system. IEEE Trans Inf Technol
  Biomed. 2003 Dec7(4)249-55.
• Crowley RS, Gadd CS, Naus G, Becich M, Lowe HJ.
  Defining the role of anatomic pathology images in
  the multimedia electronic medical record--a
  preliminary report. Proc AMIA Symp 2000161-5.
• Becich, M.J. The role of the Pathologist as
  tissue refiner and data miner The impact of
  functional genomics on the modern pathology
  laboratory and the critical roles of Pathology
  Informatics and Bioinformatics. Molec Diag.
  5(4)287-299, 2000.
• Landman A, Yagi Y, Gilbertson J, Dawson R,
  Marchevsky A, Becich MJ. Prototype web-based
  continuing medical education using FlashPix
  images. Proc AMIA Symp. 2000 462-6.
• Becich, M.J.. Information management moving from
  test results to clinical information. Clin
  Leadersh Manag Rev. 2000 Nov-Dec14(6)296-300.
• Gilbertson, J. Becich, M.J. Imaging in
  Pathology. Adv Lab Med Prof 8(1) 55-58, 1999.
• Dawson R, Gilbertson JR, Kim SJ, Becich MJ.
  Pathology Imaging on the Web Extending the Role
  of the Pathologist as Educator to Patients.
  Pathology Clinics of North America? Clinics in
  Laboratory Medicine, 19(4), Dec 1999, 849-66.
• Gilbertson, J. and Becich, MJ. Perspectives in
  Pathology Cancer Information Therapy and the
  Need to Transform the Pathology Report. Adv Lab
  Mgr June 1998.
• Wetzel, A.W., Crowley, R., Kim, S.J., Dawson, R.,
  Zheng, L., Joo, Y.M., Yagi, Y., Gilbertson, J.,
  Gadd, C.,. Deerfield, D.W., Becich M.J.
  Evaluation of prostate tumor grades by content
  based image retrieval. 27th AIPR Workshop
  Advances in Computer Assisted Recognition,
  Washington D.C., October 16, 1998., SPIE
  Proceedings, Vol. 3584, pages 244-252.
• Becich, M.J., Gilbertson, J. Perspectives in
  Pathology - Cancer information therapy and the
  pathology report. Adv Lab Med Prof 611-12, 14,
  1998.

33
Bioinformatics Publications NOTE Please e-mail
me at becich_at_pitt.edu if you want PDFs

• Zheng L, Wetzel AW, Gilbertson J, Becich MJ.
  Design and analysis of a content-based pathology
  image retrieval system. IEEE Trans Inf Technol
  Biomed. 2003 Dec7(4)249-55.
• Gupta D, Saul M, Gilbertson JR. Evaluation of a
  De-identification Software Engine to Share
  Pathology Reports and Clinical Documents for
  Research. AJCP, Feb 2004.
• Berman JJ, Datta M, Kajdacsy-Balla A, Melamed J,
  Orenstein J, Dobbin K, Patel A, Dhir R, Becich
  MJ. The tissue microarray data exchange
  specification implementation by the Cooperative
  Prostate Cancer Tissue Resource. BMC
  Bioinformatics. 2004 Feb 275(1)19.
• Yu YP, Landsittel D, Jing L, Nelson J, Ren B, Liu
  L, McDonald C, Thomas R, Dhir R, Chandran U,
  Gilbertson J, Finkelstein S, Michalopoulos G,
  Becich MJ, and Luo JH. Gene Expression
  Alterations in Prostate Cancer Predicting Tumor
  Aggression and Preceding Development of
  Malignancy. (In press, J Clin Oncol).
• Gilbertson JR, Gupta R, Nie Y, Patel AA, Becich
  MJ. Automated Clinical Annotation of Tissue Bank
  Specimens. (in press, MedInfo)
• Melamed J, Datta MW, Becich MJ, Dhir R,
  Orenstein, JM, Silver S, Fidelia-Lambert M,
  Kadjacsy-Balla A, Macias V, Walden PD, Bosland
  MC, Berman JJ, and the CPCTR. The Cooperative
  Prostate Cancer Tissue Resource (CPCTR) A
  Specimen and Data Resource for Cancer Researchers
  (in press, Clinical Cancer Research).
• Becich MJ, Gilbertson JR, Gupta D, Grzybicki DM
  and Raab, SS. Patient Safety and Healthcare
  Research The Critical Role of Pathology
  Informatics in Error Reduction and Quality
  Initiatives. (in press, Clin Lab Med).
• Yagi Y, Ahmed I, Gross W, Becich, MJ, Demetris
  AJ, Wells A, Wiley C, Michalopoulos G, Yousem S,
  Barnes B, Gilbertson J. Web-casting Pathology
  Department Conferences in a Geographically
  Distributed Medical Center (in press, Mod
  Pathol).
• Mitchell KJ, Becich MJ, Berman JJ, Chapman WW,
  Gilbertson J, Gupta D, Harrison J, Legowski E,
  and Crowley RS Implementation and Evaluation of
  a Negation Tagger in a Pipeline-based System for
  Information Extraction from Pathology Reports
  (in press, Med Info).
• Li S, Becich MJ, Gilbertson J. Microarray Data
  Mining Using Gene Ontology. (in press, Med Info)
• Ma C, Becich MJ and Gilbertson JR. Comparison of
  analysis methods, feature reduction and tissue
  processing on the supervised classification of
  benign and tumorous prostate tissue samples. (in
  press, Bioinfo.)
• Lyons-Weiler, J., Patel, Satish, S., Becich,
  M.J., Godfrey, T. An Unusual Form of
  Differential Expression in Cancers. (in press,
  Bioinfo).

34
Support APIII
10th Annual Meeting 2005 Meeting Aug
24-26th Themed on Practical Tools for
Pathology/Oncology Pathology Imaging Jamboree,
HL7 and SNOMED in Practice, HIPAA, Honest Broker
and IRB Practical Approaches, caBIG as a Change
Element, Lab Portals, Impact of EMRs, Vendor
Visions of the Future New Venue Lake Tahoe,
CA Granlibakken Conf Ctr Abstract
Sessions Trainee Travel Awards http//apiii.upmc.e
du
35
History from APIII 1997
http//apiii.upmc.edu/archive/1997/talks/becich/sl
d001.htm
36
History from APIII - 1999
http//apiii.upmc.edu/archive/1999/talks/e2/sld001
.htm
37
History from APIII - 2002
http//apiii.upmc.edu/archive/2002/Path-in-Onc-Inf
o-APIII2002-MBv3-091802x.htm
38
APIII On Line Archive
http//apiii.upmc.edu/live/index.html
39
Center for Pathology Informatics Benedum
Oncology Informatics Center

• Programming Support
• Becky Boes
• Tom Harper
• John Milnes
• Kelli Richter
• Web Services
• Valerie Monaco, PhD, MS HCI
• Aab Arnold Dan Goldberg
• Network and Server Services
• Gary Burdelski
• Ryan Mitchell
• Help Desk and Application Support
• Chuck Susanin
• Joel Young (Mac Support)
• Mark Michalski (PC support)

• Cerner CoPath AP LIS
• Bill Gross
• Anthony Piccoli
• Frank Losos
• Rick Nestler Lisa Devine
• Support staff of 12 (18 sites)
• Misys FlexiLab CP LIS
• Gary Blank, PhD
• Jim Harrison, MD PhD
• Support Staff of 8 (12 sites)
• Cerner PathNet LIMS
• Mike Sendek
• Jeff Schullo
• Support staff of 3 (5 labs)
• Quest Joint Venture Support
• Mary Mueller

40
Benedum Oncology Informatics Center and Center
for Pathology Informatics

• Bioinformatics and Analysis Group
• John Gilbertson, MD
• James Lyons-Weiler, PhD
• Deep Bhattacharya, (Grad St)
• Uma Chandran, MS
• Cathy Ma (Grad Student)
• Cancer Registry
• Sharon Winters, MS
• Heidi Gianella
• Susan Urda
• Clinical Trials
• Doug Fridsma, MD PhD
• Mike Davis
• Bill Gross
• De-Identification Software
• Melissa Saul, MS

• Organ Specific Databases
• Rajnish Gupta
• Yimin Nie, MD
• caTIES, Vocabularies and CDEs
• Rebecca Crowley, MD
• Kevin Mitchell, MS
• Tissue Banking Info System
• Michelle Bisceglia
• Vicky Chu
• Rajiv Dhir, MD
• Ashok Patel, MD
• Susan Urda
• Tissue Microarray Info Sys EDRN
• Harpreet Singh
• PGED and Gene Ontology tools

41
End of Talk e-mail me at becich_at_pitt.edu if you
have questions/clarifications not covered in the
discussion. NOTE Please e-mail if you want PDFs
of articles or presentations.

• Thank you for the invitation to participate in
  the Lab Infotech Summit Meeting.

42
Organizational Chart
Clinical Support
Translational Research
Enterprise Research Tools
Imaging Telepathology
National Member Teaching Organizations
Patient Safety Informatics
43
Molecular Imaging

• Intro to Pathology Informatics
• Opportunities and Challenges
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Molecular Pathology Electronic Medical Record
• Molecular Imaging Proteomics
• Major Threat Under Investing in Training
• Major Opportunity NIH Roadmap Initiatives
• Conclusions

44
Molecular Imaging Threat

• IHC, ISH and Molecular Testing and Proteomics
• Detect protein distribution in tissue without
  antibodies
• Uses modification of mass spectroscopy technology
  that has been used in clinical pathology labs for
  years
• The squeeze will come from two directions
• Proteomics based tissue mass spectroscopy (next
  slide)
• Radiology based biological imaging methods (more
  on this next year) focused on micron rather than
• What can we do about it in Pathology?
• Change the attitude of pathologists (anatomic,
  clinical molecular pathologists) to champion
  these new diagnostic technologies
• Break down the 6 foot thick concrete walls btwn
  AP/CP

45
Proteomics BasedMolecular Tissue Imaging

• Proteomics based Tissue Imaging Needs a Digital
  Imaging Platform whole slide imaging can
  provide this
• Allows for dynamic probing of cells and tissues
  for protein expression at micron (sub-millimeter)
  level
• Will be new tool for pathologists (hopefully)!!!

46
Molecular Pathology, Informatics Imaging - Key
Roles in Biomarker Development

• Pathologic, Genomic, Proteomic Analyses on
  Patients Tumors
• Clinical Information Outcomes
• Biomarker Patho(Bio) Informatics
• Imaging will be key in the mapping DNA, RNA and
  proteins in the context of disease to create new
  tools for theranostics

Pathologic Analysis (Phenotype)
Bioinformatics
Bioinformatics
Bioinformatics
Genomics (Genotype)
Proteomics
47
Short Intro to Pathology Informatics

• Pathology Informatics history
• Initiated 70s w/ computerization of the CP labs
• Automated Information Management in the Clinical
  Laboratory (AIMCL) launched in 1983 by Bruce
  Friedman
• Focused on system selection and lab management
• CP focused initially diversified towards AP
  bioinfo
• APIII launched in 1996 (Univ of Pittsburgh)
• CAPF creates Informatics Awards in 1997
• Association for Path Info launched in 2000
• AIMCL becomes LabInfoTech Summit in 2003

48
Over 50 Pathology Programs with Informatics
Division

• Pathology Partners Kuo Cheng
• Rutgers Dave Foran
• Scripps Memorial John Spinosa
• Stanford University Vacant
• Thomas Jefferson Univ. DeBaca
• Univ. Arizona - Weinstein, Klein
• UCLA George Thomas
• Univ. Chicago Vacant
• UMDNJ Tony Grygotis
• Univ. Maryland - Vacant
• Univ. Michigan- Bruce Friedman
• Univ. Minnesota Don Connelly
• Univ. Pennsylvania Mike Feldman
• Univ. Pittsburgh Crowley, Gilbertson, Monzon
• Univ. Rochester Scott LaPoint
• Univ. TX Houston Dan Cowan
• Univ. Toronto Aaron Pollett
• Univ. Virginia Harrison, Post
• Univ. VT Mike Gagnon

• Allegheny WP Hospital - Vacant
• Armed Forces Inst. Path. Bruce Williams
• Brigham Womens David Beckwith
• Brown University Vacant
• Chilton Memorial Hosp Dwayne Breining
• Cleveland Clinic Wally Henricks
• Creighton University Jim Healy
• Dana Farber Cancer Center Mark Rubin
• Duke University Rajesh Dash
• East Carolina University Paul Catrou
• Emory University - Hunter Hardy
• Fred Hutchinson Cancer Ctr. Vacant
• Florida State University Ed Klatt
• Geisinger Health System Jeff Prichard
• Harvard Univ. Dave Weinberg
• Henry Ford Mark Tuthill
• John Hopkins University Bob Miller
• Massachusetts General Hospital Ul Balis
• MD Anderson Langford, Mark Routbort

NOTE CAPF Travel Awardees 14 active as
faculty in Pathology Informatics.
NOTE At least 10 programs are seeking
Pathology Informatics Faculty
49
Pathology Informatics Where Should We Focus

• AP- CP-LIS Integration Problems
• Informatics Challenges in Molecular Pathology
• The AP vs. CP issue
• Where are New Technologies Leading Us?
• Role of Tissue Banks in Molecular Testing
  Research
• Need more involvement in Electronic Med Record
• Major Threat Under Investing in Training and
  Certification in Informatics (e.g. Pathology)
• Major Opportunity NIH Roadmap ONCHIT
• Conclusions APIII, LabInfoTech and API are
  having a major impact thanks to your support!!!

50
Association for Pathology Informatics
(API)http//www.pathologyinformatics.org
to advance the field of pathology informatics as
an academic and a clinical subspecialty of
pathology.
51
API Membership Benefits to Individuals

• Recognition as member of Pathology Informatics
  professional discipline
• Discounts at APIII, Lab InfoTech meetings
• Attending one meeting alone offsets cost of dues
• Members-only Listserv
• Managed at ASIP (advertisement-free)
• Peer networking and education (real time)
• Professional development opportunities
• Access to open source tools
• TMA XML data exchange standard
• Comprehensive abbreviations repository
• Digital imaging standards efforts for pathology

52
API and the TMA XML Data Exchange Format

• TMA slide set contains
• Cancer tissue from radical prostatectomy
  specimens of 299 patients
• Control non-neoplastic tissue from benign
  prostatic hyperplasia (BPH)
• Control non-diseased tissue from organ donor
  prostates
• Cores from prostate cancer cell lines
• TMA XML Data Exchange Format

TMA XML Data Exchange Format for Core N52

• ltrecordgt
• ltIMS_Case_Identifiergt1033477551lt/IMS_Case_Identif
  iergt
• ltLocation_CodegtN52lt/Location_Codegt
• ltRacegtCaucasianlt/Racegt
• ltYear_of_Birthgt1923lt/Year_of_Birthgt
• ltYear_of_Diagnosisgt1991lt/Year_of_Diagnosisgt
• ltYear_of_Prostatectomygt1992lt/Year_of_Prostatectom
  ygt
• ltIs_Residual_Carcinoma_PresentgtYeslt/Is_Residual_Ca
  rcinoma_Presentgt
• ltMost_Prominent_Histologic_Typegtadenocarcinoma
  NOS aka acinarlt/Most_Prominent_Histologic_Typegt
• ltGleason_Primary_Gradegt4lt/Gleason_Primary_Gradegt
• ltGleason_Secondary_Gradegt3lt/Gleason_Secondary_Grad
  egt
• ltGleason_Sum_Scoregt7lt/Gleason_Sum_Scoregt
• ltNumber_of_Nodes_Examinedgt11lt/Number_of_Nodes_Exam
  inedgt
• ltNumber_of_Nodes_Positivegt0lt/Number_of_Nodes_Posit
  ivegt
• ltDistant_Mets__1_at_Time_of_DiagngtNonelt/Distant_Me
  ts__1_at_Time_of_Diagngt
• ltpT_StagegtpT3alt/pT_Stagegt
• ltpN_StagegtpN0lt/pN_Stagegt
• ltpM_StagegtpM0lt/pM_Stagegt
• ltVital_StatusgtAlivelt/Vital_Statusgt