1	API Focus Session Oncology Informatics: Organizational and Strategic Goals Lab Infotech Summit – The Venetian, Las Vegas, NV Friday, March 12^th, 2004 Michael J. Becich, MD PhD (becich@pitt.edu) Vice Chairman and Professor of Pathology, Professor of Information Sciences & Telecommunications University of Pittsburgh School Medicine Director, Center for Pathology Informatics http://path.upmc.edu/cpi Director, Benedum Oncology Informatics Center (Hillman Cancer Center) http://www.upci.upmc.edu/internet/benedum/index.html Course Co-Director, APIII or Advancing Practice, Instruction and Innovation through Informatics (http://apiii.upmc.edu)
2	Disclosures by MJB Corporate Support for API and APIII 650K projected for 2004 [Cerner, Misys, IBM, IMPAC - Tamtron (formerly ImPath), GE/Triple G, Apollo, Ardais, Cisco, Verizon, CAP Today, Amersham, Applied Imaging, Chromavision, Nikon, Olympus, DakoCytomation, Ikon, General Data, Trestle, SCC Soft, Sysmex, Neural Ware, SNOMED, PSA, Thermo Shandon, Milestone SRL, Soft Genetics and others] Corporate Sponsored Research Agreements 1.3M in 2003 [Amgen, Diadexus, Genelogic, IBM, InterScope Technologies (MJB founder equity), HLR, Millennium, Nikon, Paradigm Genetics (MJB founder equity)] Startup/Public Companies (Founder Equity - MJB): InterScope Technologies, Inc. (http://www.interscopetech.com) Provider of high speed/volume microscopic imaging/telepathology systems Venture-MAVF, Birchmere & Cape Andover. Ultrarapid digitization: Gb data transfers, terabyte storage and robotics Paradigm Genetics (NASDAQ:PDGM, formerly TissueInformatics, Inc., merged 03/01/04, http://www.tissueinformatics.com) Systems biology services (CRO) to pharma- & agra- biotech Venture backed TVM, Motorola. Hyperquantitative image analysis & genomics capabilities.
3	Outline Introduction to Oncology Informatics Organizational and Strategic Goals Modified Association of American Cancer Institute (AACI) survey to evaluate Oncology Informatics Infrastructure How Pathology Informatics can be leveraged? Why Pathology Informatics has lagged and how we can fix it. Cancer Biomedical Informatics Grid Initiative (caBIG – http://cabig.nci.nih.gov Conclusions
4	Hillman Cancer Center University of Pittsburgh Cancer Institute and UPMC Cancer Centers Comprehensive Cancer Center 12 patient care centers, each focusing on a specific type or treatment of cancer Nationally/Regionally Competitive Institution size 600+ physicians/researchers Number of new cancer patients per year: 10,000+ Participation in clinical trials Number of open protocols by sponsor type 247 active protocols (48 pharmaceutical, 28 cooperative group, 45 internal) Number of patients accrued in 2003: 1128
5	UPMC Cancer Centers Cancer Registry Index Cases 2003
6	Pennsylvania Cancer Centers – The Opportunity
7	Cancer Informatics Services at U Pitt Software Solutions Supported and Deployed: Clinical Trials Software – Developed at U Pitt = Clinical Trials Mgmt App Deployed at Hillman Cancer Center, UPMC Presbyterian/Shadyside Deployments underway at regional cancer centers and physician offices Deployments planned at Magee Women’s, Children’s Hospital Cancer Registry Software – MRS system of IMPAC (formerly ImPath) Deployed in 12/18 hospitals (homogeneous consolidation); Outpatient rollout Tissue Banking Info System (URL behind firewall) at 8 cancer centers Organ Specific Databases (URL behind firewall) – identify tissue samples Virtual Specialty Microarrays (URL behind firewall) – at U Pitt only Gene Expression Databases (public, see below) – at U Pitt only Support of Clinical Systems: LIMS (Cerner, Affy and Amersham), Cerner CoPath (Anatomic Path), Misys (Clinical Path), Cerner (Electronic Medical Record) Web Tools (including extensive web casting): UPMC Cancer Centers Website (see http://www.upmccancercenters.com) UPCI Website (see http://www.upci.upmc.edu) Bioinformatics (see http://bioinformatics.upmc.edu/index.html) Honest Broker for Tissue, Data and Outcomes Research (handout) Certified Honest Brokers include: 7 Tissue Bankers (Pathology Assistants and Support Staff), 5 Cancer Registrars & 3 Outcomes Researchers–Nurse Coordinator and Data Managers
8	Market Validation of Oncology/Pathology Synergy Merger and Acquisition activity in 2000’s – Validation? AP-, CP-Lab Information Systems (LIS) and Oncology Systems IMPAC’s purchase of Tamtron (AP-LIS) & MRS Registry (CR) LabCorp’s purchase of Dianon/Urocor (AP and Clinical Trials) Misys’s (CIS) purchase of Sunquest (CP-LIS) Cerner’s (CIS) purchase of Dynamic Healthcare Tech. (APLIS-CoPath) Biotech Startups – More Validation through VC & Mergers Paradigm Genetics merger with TissueInformatics – Systems Biology approach combining Genomics with Image Analysis LifeSpan Biosciences – Strategic Partnership with NEC (Japan) = CRO for tissue based discovery with high end imaging strategy Ardais – >$20M VC – Tissue Banking for Pharma and Biotech Aureon – >$20M VC – ‘Gold Standard’ Molecular AP Testing, Database Validation Pathogenomics – >$20M VC – Genomic Discovery on Tissue Bank
9	Strategic Approach – Why Oncology Informatics? Successful launch of Pathology Informatics (1991-2001) Over 70 identified Pathology Informatics Divisions Two successful national meetings (APIII and Lab Infotech Summit) Major problems: Path is under reimbursement pressure, systems considered “mature” Lack of sustained independent extramural funding Why Oncology Informatics as the next step forward? Higher profile in the clinical arena (70/70/70 rule) Lack of developed systems, people, focus to support Oncology Unlike Pathology, Oncology Informatics has significant support: Service lines at most major medical centers – very profitable, growing NCI has launched several strategic IT initiatives: NCICB – NCI Center for Bioinformatics (5 FTEs 5 years ago now >80) caBIG – Cancer Biomedical Informatics Grid Initiative – $100M (with NCICB) NBN – National Biospecimen Network (cChange and NCI backed) – $100M Cancer Centers Program is highly supported by new director Cancer Center Support Grants represent 20% of NCI funding base Part of the NIH Roadmap – Informatics key to research/clinical success
10	Outline Introduction to Oncology Informatics Organizational and Strategic Goals Modified Association of American Cancer Institute (AACI) survey to evaluate Oncology Informatics Infrastructure How Pathology Informatics can be leveraged? Why Pathology Informatics has lagged and how we can fix it. Cancer Biomedical Informatics Grid Initiative (caBIG – http://cabig.nci.nih.gov Conclusions
11	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation Association of American Cancer Institutes (AACI) Information Technology Initiative: Launched 1/01 with 19 individuals from 18 Cancer Centers with focus to define a roadmap for to rate their Informatics Infrastructure
12	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation AACI Information Technology Initiative: Composed of 4 subcommittees:
13	Cancer Center Informatics Capabilities - Priming the Clinical Practice Engine Level One: 1. Provides both structured (e.g. relational database tables) and unstructured (e.g. free text history) data storage. 2. Data integrity procedures and redundancy of data storage, real-time recovery capabilities and disaster recovery. 3. Data systems are secure, meet HIPAA, JCAHO, and other standards, and protect patient confidentiality. Methods are in place for de-identification of patient data and for secure transmission of data within and between health care institutions. 4. A generic and flexible relational database design. 5. ‘Help Desk’ support is in place 24X7, and backup technical teams are on call at all times for clinical and critical research systems.
14	Cancer Center Informatics Capabilities - Expanding Clinical Practice Connectivity Level Two: All of the features defined in Level One, plus: 1. A scalable, sustainable architectural plan for management. 2. Adherence to institutional, national and/or NCI standards. 3. When suitable standards do not exist, data formats are developed in collaboration with other groups conducting similar research. 4. Interfaces among systems have been established and maintained. 5. Multiple means of data entry are in use (data sharing, Web, etc.) 6. Tools have been developed to help analyze, summarize, monitor, and display data for purposes of quality and clinical care delivery. 7. “Self-aware” system monitoring of network and applications. 8. Policies/procedures to optimize data quality and minimize data 9. Bad data management areas have been identified and eliminated. 10. A Master Patient Index (MPI) is in place for the enterprise. 11. Monitoring of clinical and research system performance. 12. Standards for data content (vocabularies), transport (HL7, XML), and storage (relational database) are in place.
15	Cancer Center Informatics Capabilities - Supporting the Electronic Health Record Level Three: All of the features in Level Two, plus: 1. A clinical data repository (“electronic medical record”) is in place to reliably store and make available data for patient care, including all information known at a CC about the patient. This repository should ideally be based outside of any vendor’s legacy system to enable upgrades and changes of vendor products and applications that are transparent to the user, and should have applicability to research where needed. 2. The clinical systems of the CC are integrated with those of the larger institution within which it is located, e.g. such that Oncology Ward data are shared with the ICU and vice versa. 3. Integration of clinical systems for research purposes is fully in place, with data extraction from legacy systems occurring in an automated manner, with appropriate sampling to verify data quality.
16	Cancer Center Informatics Capabilities - Research Information Services Quality Triad Level Four: All of the features in Level Three, plus: 1. Systems provide educational and decision-support benefits while reducing errors, improving quality and being cost-effective. 2. Standardized/automated “order sets”& templates to reduce errors. 3. Computer assisted decision support systems for med orders. 4. Computer systems check for allergies and drug interactions. 5. Computer systems support alerting through monitoring of laboratory values. 6. Keystroke level log files for review for safety, quality, etc… 7. Research systems provide decision support. 8. Systems are in place to facilitate clinical care delivery and interfacing to research systems to provide data captured at POC. 9. Clinical/research systems have “production”, “training”, and “development and test” platforms which are separate.
17	Cancer Center Informatics Capabilities - Developing an Innovative Research Infrastructure Level Five: All features of Level Four, plus: 1. A “research data warehouse” is in place that on a routine schedule automatically extracts data useful to research from several legacy “feeder” systems (clinical trials database, EMR, financial, laboratory, genetic, cancer registry, etc.), performs quality control checks, transforms the data for compatibility, de-identifies the patients, and loads the data into the long term data repository store. With appropriate IRB approval investigators are provided access to the warehouse data to perform longitudinal studies, case series, outcomes research, genotype-phenotype correlations, and data mining to discover new etiologic or prognostic factors.
18	A Roadmap for Progressive Growth = Overview of AACI Informatics Initiative Level 1 – Priming the Clinical Practice Engine Supporting the central database (no commercial IS for Oncology). Infrastructure support (server/network) and establishment of help desk. Level 2 – Expanding Clinical Practice Connectivity Architectural plan with interfaces, MPI, data entry tools, standards for data content, transport and storage; ‘self aware’ performance monitoring. Level 3 – Supporting the Electronic Health Record (EHR) EHR integrated with Oncology with automated data extraction. Level 4 – Research Information Services Quality Triad Clinical and research decision-support in place reducing errors, improving quality and promoting cost-effective computing (TPS triad). Level 5 – Developing an Innovative Research Infrastructure Research data warehouse supporting clinical, basic & translational study.
19	AACI Survey of Informatics Capabilities
20	Outline Introduction to Oncology Informatics Organizational and Strategic Goals Modified Association of American Cancer Institute (AACI) survey to evaluate Oncology Informatics Infrastructure How Pathology Informatics can be leveraged? Why Pathology Informatics has lagged and how we can fix it. Cancer Biomedical Informatics Grid Initiative (caBIG – http://cabig.nci.nih.gov Conclusions
21	Pathology Informatics and the Hillman Cancer Center Pathology Informatics (12 faculty; GB, MB, RC, RD, DGx2, DJ, JG, JH, SR,YY & 1 TBN; and 85 staff) 18 hospitals – one anatomic laboratory information system (LIS = Cerner CoPathPlus and PathNet LIMS) 12 of 18 hospitals - one clinical pathology LIS (= Misys) One tissue banking plan (5 major tissue banks) and now linked to our Organ Specific Database program Organ Specific Databases and Virtual Specialty MicroArrays (research information systems) Oncology Informatics (3 faculty; JLW, VM & 1 TBN; and 20 staff) 12 of 18 hospital on one Cancer Registry (ImPath) Number of new index cancer patients per year: 10,000+ Clinical Trials Information System (developed by Oncology Informatics)
22	Leveraging Our Hard Work in Pathology Informatics Why Pathology Informatics has a lot to offer Oncology Inforamtics: Advancing Practice, Instruction & Innovation through Informatics (APIII) National Meeting focused on Biomedical Informatics (emphasis shifted to oncology informatics and bioinformatics from pathology informatics) Over 2500 attendees to date over 8 years Over 375 trainees to date over 8 years via CAP Foundation Program Includes vendor community critical to long term support and R&D Relationships with American Medical Informatics Association (AMIA), College of American Pathologist (CAP), in discussions with American Association for Cancer Institutes (AACI) National Member Organizations (Hilliard) Association for Pathology Informatics (API – see http://apiii.upmc.edu) American Society for Investigative Pathology (ASIP) American Telemedicine Society Special Interest Group in Telepathology Training program coordinated centrally (Harrison) APIII Travel Awardees – over 350 and CAP Technology in Training Informatics Awardees – 6 MS and PhD Students – 10 NLM Biomedical Informatics Fellows – 8 and Informatics Fellows - 13 Bioinformatics Trainees – 6 Web Properties – over 3M hits per month from over 350K unique visitors
23	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation (Modification for Pathology Informatics) Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting the Clinical Pathology Laboratory Info System (next year) Distinguishing IT support from Pathology Informatics Level 2 – Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc… Synoptics, report generation, remote computing, imaging and voice. Level 3 – Supporting the Instructional Mission Web tools, on-line training materials, web cast, specialty training needs Level 4 – Supporting Research Information Services Tissue banking, tissue microarrays, de-identification, honest broker Level 5 – Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes
24	Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Lab Info Sys (LIS) Automated Histology, Special Procedure and Consult Billing & Reporting Reengineering of Transcription, Electronic Signout/Signature “Nuclear” Reporting – All special procedures/testing linked AP report Synoptic Reporting, Templates and Macros for the Quality Triad Bar Coding, Telephone Interface, Immunostaining/Histology Interfaces Supporting the Clinical Pathology Laboratory Info System Next Year (Robotics with a Pre-Analytical focus, POE, Decision Support) Owning the Reporting, EHR integration, POC testing, Test Integration Interfaces Development and Pricing Strategy (currently over 130) Lab Portal, Web Reporting, “Virtual” Lab, Compliance/Regulatory FX Distinguishing IT support from Pathology Informatics ‘Help Desk’ support is in place 24X7, and backup technical teams are on call at all times for clinical and critical research systems. Outsource to central IT – network, desktop, and PC/peripheral support Focus on Application Support and Research Support
25	Component Technology – Pathology & Oncology Informatics Pathology Informatics Anatomic Pathology Clinical Pathology Hematopathology & Molecular Diagnostics LIMS for Genomics and Proteomics Tissue Banking Telepathology Web Site Support Oncology Informatics Cancer Registry Clinical Trials Organ Specific Program Support Prostate, Melanoma, etc.. Telemedicine (for oncology) Web Site Support E-Health Initiatives
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27	Information Model for Bioinformatics Support of the Pancreas SPORE
28	Tissue and Data Integration Support Plan for Bioinformatics
29	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting the Clinical Pathology Laboratory Info System (next year) Distinguishing IT support from Pathology Informatics Level 2 – Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc… Synoptics, report generation, remote computing, imaging and voice. Level 3 – Supporting the Instructional Mission Web tools, on-line training materials, web cast, specialty training needs Level 4 – Supporting Research Information Services Tissue banking, tissue microarrays, de-identification, honest broker Level 5 – Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes
30	Level 2 - Clinical Practice Connectivity and the Quality Triad Interfaces and Integration Issues EHR (Electronic Health/Medical Record) – Key Partnership Required 70/70/70 Rule – 70% of health care data in EMR is Pathology data (probably more!!!) 70% of medical decisions/critical health care events involve Pathology date 70% of all pathology testing is oncology related QA/QC/QI programs – A new initiative to reduce errors Toyota Production Systems and the Quality Triad (I’m a believer!!!) Synoptics, Templates, “Quick Text”, etc Role of common data elements and ISO Standardized report CAP Cancer Checklists SNOMED CT Report generation – AutoFAX, EHRs, interface 3^rd party sys Remote computing – VPN, Citrix, remote transcription Imaging - Digital Libraries concept not just telepathology Imaging integration, whole slide imaging and quantitative image analysis
31	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting the Clinical Pathology Laboratory Info System (next year) Distinguishing IT support from Pathology Informatics Level 2 – Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc… Synoptics, report generation, remote computing, imaging and voice. Level 3 – Supporting the Instructional Mission Web tools, on-line training materials, web cast, specialty training needs Level 4 – Supporting Research Information Services Tissue banking, tissue microarrays, de-identification, honest broker Level 5 – Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes
32	Level 3 – Supporting the Instructional Mission Web tools, laptop resources and educational support Develop fully functional web portal and chief resident information system Residents web resource (see http://residents.pathology.pitt.edu) On-line training materials Virtual slides sets (see http://virtualslide.upmc.edu) Web casts All diagnostic conferences, resident didactic conferences and other CME venues are web cast (see http://path.upmc.edu) Intelligent Tutorial Systems Tutors for medical students Tutors for residents and fellows Specialty training needs Digital solutions for board review, residency competency evaluation, etc.
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34	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting the Clinical Pathology Laboratory Info System (next year) Distinguishing IT support from Pathology Informatics Level 2 – Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc… Synoptics, report generation, remote computing, imaging and voice. Level 3 – Supporting the Instructional Mission Web tools, on-line training materials, web cast, specialty training needs Level 4 – Supporting Research Information Services Tissue banking, tissue microarrays, de-identification, honest broker Level 5 – Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes
35	Tissue and Data Integration Support Plan for Bioinformatics
36	A Roadmap for Progressive Growth = Advancing Practice, Instruction & Innovation Level 1 – Priming the Clinical Practice Engine Supporting the Anatomic Pathology Laboratory Information System Supporting the Clinical Pathology Laboratory Info System (next year) Distinguishing IT support from Pathology Informatics Level 2 – Clinical Practice Connectivity and Quality Triad Interfaces and Integration Issues with EMR, QA/QC/QI programs, etc… Synoptics, report generation, remote computing, imaging and voice. Level 3 – Supporting the Instructional Mission Web tools, on-line training materials, web cast, specialty training needs Level 4 – Supporting Research Information Services Tissue banking, tissue microarrays, de-identification, honest broker Level 5 – Developing an Innovative Research Program Bioinformatics, imaging, decision support, cancer informatics, outcomes
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39	Tissue and Data Integration Support Plan for Bioinformatics
40	Organ Specific Databases Developed to support tissue banking, the OSD warehouse integrates data from multiple clinical systems to provide de-identified, clinomic data sets for UPCI researchers and inter-institutional collaborators. Supports research in patient safety, tissue banking, gene expression and imaging. Data Marts in Prostate, Melanoma & Breast In planning stages for Lung, GI, Heme, Ovary & Uterine
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42	Support for Cancer Informatics Services
43	Outline Introduction to Oncology Informatics Organizational and Strategic Goals Modified Association of American Cancer Institute (AACI) survey to evaluate Oncology Informatics Infrastructure How Pathology Informatics can be leveraged? Why Pathology Informatics has lagged and how we can fix it. Cancer Biomedical Informatics Grid Initiative (caBIG – http://cabig.nci.nih.gov Conclusions
44	caBIG – 5 Development Areas http://caBIG.nci.nih.gov
45	Outline Introduction to Oncology Informatics Organizational and Strategic Goals Modified Association of American Cancer Institute (AACI) survey to evaluate Oncology Informatics Infrastructure How Pathology Informatics can be leveraged? Why Pathology Informatics has lagged and how we can fix it. Cancer Biomedical Informatics Grid Initiative (caBIG – http://cabig.nci.nih.gov Conclusions
46	The Value of Oncology, Pathology & Bioinformatics in the Cancer Center Oncology in partnership with Pathology have a great future. The importance of tissue and serum banks has never been higher. Too many pathologists consider themselves “experts who look at glass slides and test values” not “experts in analysis of tissue/serum in disease and customized theranostics”. What could we accomplish if we could apply integrated bioanalytical methods and standardized data capture to the classification of cancer?
47	Importance of APIII
48	History from APIII 1996
49	History from APIII 1998
50	History from APIII - 1999
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53	Benedum Oncology Informatics Center and Center for Pathology Informatics 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
54	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 Software Doug Fridsma, MD PhD Mike Davis Bill Gross De-Identification Software Melissa Saul, MS Dilip Gupta, MD and others
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56	Recent Publications by Our Team 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. Becich, MJ. Information Management: Moving from Test Results to Clinical Information. 2000. Clinical Lab Management Review. Clinical Lab Mgmt Rev 14(6): 296-300, 2000. Yu, Y.P.; Lin, F.; Bisceglia, M.; Krill, D.; Dhir, R.; Becich, M.; Luo, J-H. Short Communication: Identification of a novel gene with increasing rate of suppression in high grade prostate cancers. Am J Pathol Jan;158(1):19-24, 2001. Lin, F.; Yu,Y.P.; Woods,J.; Cieply,K.; Gooding,W,l Finkelstein, P.; Dhir, R.; Krill, D.; Becich, M.J.; Michalopoulos, G.; Finkelstein S.; Luo, J.H. Myopodin, a novel synaptopodin homologue, is frequently deleted in invasive prostate cancers. Am. J. Pathol, Nov; 159(5):1603-12, 2001. Luo, J.H..; Yu, Y.P.; Cieply, K.; Lin, F.; Deflavia, P.; Dhir, R.; Finkelstein, S.; Michalopoulos, G.; Becich, M.J.. Gene Expression Analysis of Prostate Cancers. Mol. Carcinog, Jan;33(1): 25-35 2002. Zheng. L., Wetzel, A., Gilbertson, J., Becich, M.J.; Design and Analysis of a Content-Based Pathology Image Retrieval System, IEEE January 3, 2002 Bunker, C.H.; Patrick, A.L.; Konety, B.R.; Dhir, R.; Brufsky, A.M.; Vivas, C.A.; Becich, M.J.; Trump, D.L.; Kuller, L.H.; High Prevalence of Screening-Detected Prostate Cancer Among Afro-Caribbeans: The Tobago Prostate Cancer Survey, Cancer Epidemiol, Biomarkers Prev.Aug;11(8):726-9, 2002. 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 Gilbertson JR, Gupta R, Nie Y, Patel AA, Becich MJ. Automated Clinical Annotation of Tissue Bank Specimens. Proc MedInfo, 2004 Yagi, Y, Ahmed, I, Gross, W, Becich, MJ, Demetris, J, Wells, J, Wiley, C, Michalopoulos, G, Yousem, S, Barnes, B, Gilbertson, J. Web-casting Pathology Department Conferences in a Geographically Distributed Medical Center. Hum Pathol. 2004 (in press).