Division of Pathology Informatics -
Project Descriptions



  • Ishtiaque Ahmed - Pathology Systems Specialist
  • Jon Duboy - PC Consultant I

Traditionally, Telepathology has been implemented using custom software and high-speed private data networks. Internet and the World Wide Web offer a flexible, ubiquitous, user-friendly, and intrinsically multimedia environment ideally suited for TelePathology applications. A basic TelePathology system includes microscope with digital camera, computer, telecommunications link between sending and receiving sites, and a workstation at the receiving site with a high-quality monitor to view the images. There also might be hardware to allow the receiving pathologist to control the microscope from a distance and view the entire slide in real-time. The images are viewed on a computer screen, rather than through microscope.

At the UPMC, we are actively investigating seven aspects of TelePathology, Telemedicine and Pathology Imaging:

  • TV Conference
    Ten TV Conference Systems have been installed at UPMC in PA. Two different type of systems (Pathology Station and View Only Station) were prepared for different purposes. We chose hardware and software very carefully for each location.
  • Web Cast Conference
    We provide live/archived three Pathology Weekly Conference on the web. Currently, the following 5 conferences are available as live and archived video.
  • Web Based Telepathology System
    Follow the link above for details.
  • Internet Based Transplant Pathology Consortium
    Follow the link above for details.
  • Robotic Microscopy
    With two robotic microscopes, UPMC's Department of Pathology has a number of ongoing projects evaluating this technology, as well as agreement with several companies to participate in the development of their products. This project is appropriate for residents and students. Our Robotic Microscopy System can support Nikon E1000 and Olympus AX80.
  • Virtual Telepathology (Virtual Microscopy) Faculty at the Division of Pathology Informatics and Pittsburgh Supercomputing Center have developed an robot to image entire slides automatically, rapidly and at high resolution. Built by an outside company, the system will undergo trials at UPMC this year. The system includes an imaging robot, slide handling robot, LIS integration and secure web viewer (virtual microscope).
  • Mobile Telepathology
    Mobile TelePathology by Pocket PC. Mobile TelePathology using VAIO GT3. Follow the link above for details.
  • Imaging Research
    Wireless WAN/LAN. IP Telephony. Multi-Spectral Imaging. Network Camera. Automated Scoring System. Dermatology. Emergency Medicine. Follow the link above for details.

Active Research Support

  • Integrated Medical Information Technology (US Air Force Telemedicine)
  • Natural Vision Project, Japanese Ministry of Public Management, Home Affairs, Posts and Telecommunications
  • HU-UPCI Cancer Education/Career Development Partnership (NCI P20 CA101592)

Molecular Reclassification of Prostate Cancer


  • Michael Becich, MD, PhD - Tissue bank and informatics lead

The aims of this project include extending the Western Pennsylvania Prostate Tissue bank to provide a national resource for research, and to analyze gene expression patterns in cancer of the prostate in comparison with non-tumor and BPH tissue to establish correlations with distinct subsets of cancer behavior. The studies will allow us to establish a molecular reclassification of prostate cancer based on coordinated expression of groups of specific genes. Complete prostatectomy specimens available from the Tissue Bank will be processed by microdissection and used to extract RNA. This will in turn be processed for analysis through the Affymetrix gene chip set. Our tissue bank contains complete and well stratified information that will be used by the bioinformatics teams of HLR and Pitt to provide correlation between coordinated expression of specific gene sets and distinct tumor behavior.

Active Research Support

  • National Cancer Institute: U01 CA86735-01 A Shared Resource for the Molecular Classification of Prostate Cancer; Comprehensive Prostate Cancer Tissue Resource (CPCTR) Consortium National Cancer Institute, 04/01/00-03/31/05, total direct costs $2,992,038.
  • National Cancer Institute: U01 CA88110-02 Molecular ReClassification of Prostate Cancer; Director's Challenge for the Molecular Classification of Cancer Consortium, 08/29/00-01/31/05, total direct costs $2,975,486.

SlideTutor - An Intelligent Tutoring System in Pathology


  • Rebecca Crowley, MD, MS - Principal Investigator
  • Drazen Jukic, MD, PhD - Dermatopathology Domain Expert
  • Olga Medvedeva, MS - Lead Developer
  • Elizabeth Legowski - Research Associate
  • Ellen Roh, MD - Postgraduate Fellow
  • Eugene Tseytlin, MS - Developer

We are adapting the well-described paradigm of the Model Tracing Intelligent Tutoring System (MT-ITS) to develop multimedia, knowledge-based pathology training systems in several domains. ITS are adaptive, instructional systems that seek to emulate the well known benefits of one-on-one tutoring when compared to other instructional methods. Very few ITS have been developed in medical domains. Our system is designed to provide individualized coaching to students as they search, and interpret virtual pathology slides. The model-tracing aspect of the system guides the student to correctly search a slide, identify relevant evidence, and formulate and test hypotheses before making a diagnosis. As the student works through each case, the system’s model-tracing mechanisms student attention, offer hints based on the student’s progress through the problem, and correct errors in the intermediate steps leading to a diagnosis. Across multiple problems or cases, the system’s knowledge-tracing mechanisms update the system’s assessment of the current state of the student’s skills. Pedagogic decisions about tutorial strategy, case selection or curriculum sequencing can then be based on the current state of the student model. The SlideTutor project includes both development and evaluation efforts, and therefore requires a cross-disciplinary team including individuals with expertise in artificial intelligence, education, cognitive science, evaluation, and Pathology. SlideTutor is funded by grants from the National Library of Medicine and National Cancer Institute.

Active Research Support

  • National Library of Medicine 1R01 LM007891-01, 7/1/03-6/30/07 $269,725 per year (Direct and Indirect).
  • National Cancer Institute 1 R25 CA101959-01, 8/24/03-8/23/06 $232,489 per year (Direct and Indirect).

The Shared Pathology Informatics Network


  • Michael Becich, MD, PhD (Site PI)
  • Rebecca Crowley, MD, MS
  • Kevin Mitchell, MS - Lead Developer
  • John Gilbertson, MD

The Shared Pathology Informatics Network (SPIN) is a National Cancer Institute (NCI) sponsored cooperative agreement among four institutions (Harvard University, University of Indiana, UCLA, and Uni-versity of Pittsburgh) to develop a model web-based system for accessing pathology data on archived human tissue speci-mens, across multiple institutions and databases. An important and difficult aspect of this work is the extraction of information (such as the diagnosis, findings, and relationship of tissue blocks to the specimen) from the free-text of Surgical Pathology reports. Information Extraction from pathology reports is complex. For example: (1) reports contain multiple sections (such as Final Diagnosis, Gross Description, Comment, etc) that vary in narrative structure and uniformity (2) there is institutional variation in reporting practices (such as differences in the keywords that delimit important sections of the report), and (3) reports contain negative as well as positive findings and diagnoses. We are working on the development of a pipeline-based system for machine annotation of surgical pathology reports. The system is built with GATE - an open source architecture for Language Engineering, available through the University of Sheffield (http://gate.ac.uk/). De-identified reports and annotations are available for retrieval by all SPIN institutions through a peer-to-peer network available developed by other consortium members. Current development efforts include (1) refinement of our negation tagger, (2) development of general methods for attribute-value extraction, (3) rule based methods for identifying common data elements to be extracted and (4) development of a human annotated corpus for use in performance testing and eventually system training.

Active Research Support

  • National Cancer Institute: UO1 CA91338-01, 08/13/01-07/31/06, total direct costs $1,376,310.