title: Automated prostate tissue referencing for cancer detection and diagnosis.
abstract: BACKGROUND
  The current practice of histopathology review is limited in speed and accuracy. The current diagnostic paradigm does not fully describe the complex and complicated patterns of cancer. To address these needs, we develop an automated and objective system that facilitates a comprehensive and easy information management and decision-making. We also develop a tissue similarity measure scheme to broaden our understanding of tissue characteristics.
  
  
  RESULTS
  The system includes a database of previously evaluated prostate tissue images, clinical information and a tissue retrieval process. In the system, a tissue is characterized by its morphology. The retrieval process seeks to find the closest matching cases with the tissue of interest. Moreover, we define 9 morphologic criteria by which a pathologist arrives at a histomorphologic diagnosis. Based on the 9 criteria, true tissue similarity is determined and serves as the gold standard of tissue retrieval. Here, we found a minimum of 4 and 3 matching cases, out of 5, for ~80 % and ~60 % of the queries when a match was defined as the tissue similarity score ≥5 and ≥6, respectively. We were also able to examine the relationship between tissues beyond the Gleason grading system due to the tissue similarity scoring system.
  
  
  CONCLUSIONS
  Providing the closest matching cases and their clinical information with pathologists will help to conduct consistent and reliable diagnoses. Thus, we expect the system to facilitate quality maintenance and quality improvement of cancer pathology.
authors.affiliation: Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea.
authors.forename: Jin Tae
authors.initials: JT
authors.lastname: Kwak
authors.affiliation: Tissue Array Research Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20850, USA.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Hewitt
authors.affiliation: Department of Pathology, University of Illinois at Chicago, Chicago, IL, 60612, USA.
authors.forename: André Alexander
authors.initials: AA
authors.lastname: Kajdacsy-Balla
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, 2122 Siebel Center, 201 N. Goodwin Avenue, Urbana, IL, 61801, USA. sinhas@illinois.edu.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Sinha
authors.affiliation: Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Mechanical Science and Engineering, Electrical and Computer Engineering, Chemical and Biomolecular Engineering and University of Illinois Cancer Center, University of Illinois at Urbana-Champaign, 4265 Beckman Institute 405 N. Mathews Avenue, Urbana, IL, 61801, USA. rxb@illinois.edu.
authors.forename: Rohit
authors.initials: R
authors.lastname: Bhargava
mesh_terms: D001331:Automation; D016208:Databases, Factual; D006801:Humans; D008297:Male; D060787:Neoplasm Grading; D011467:Prostate / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D015203:Reproducibility of Results
keywords: Database; Decision support; Infrared imaging; Prostate cancer; Tissue morphology; Tissue retrieval