• DocumentCode
    1915627
  • Title

    Development of a neural network derived index for early detection of prostate cancer

  • Author

    Zhang, Zhen ; Zhang, Hong

  • Author_Institution
    Med. Univ. of South Carolina, Charleston, SC, USA
  • Volume
    5
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    3636
  • Abstract
    ProstAsure is a neural network-derived algorithm which analyzes the profile of multiple serum tumor markers and produces a single-valued diagnostic index (ProstAsure Index, or PI) for early detection of prostate cancer (CaP). PI has been validated through multiple clinical studies with a fairly large number of blind independent test patients and has become the first of such tests commercially available through reference laboratories in the US and other countries as a clinical information processing service. We first describe briefly the development of the PI algorithm with a summary of clinical study results comparing PI with the currently accepted CaP detection tools. We then focus the discussion on two issues in developing a neural network-based clinical diagnostic system: 1) constructing training datasets under clinical constraints; and 2) estimating generalization performance by gauging the shape and “smoothness” of decision boundary surfaces of a derived classification system
  • Keywords
    cancer; learning (artificial intelligence); medical diagnostic computing; multilayer perceptrons; patient diagnosis; pattern classification; clinical information processing service; decision boundary surfaces; generalization performance; multiple serum tumor markers; neural network derived index; neural network-based clinical diagnostic system; prostate cancer; single-valued diagnostic index; Cancer detection; Data preprocessing; Diseases; Input variables; Medical diagnostic imaging; Neoplasms; Neural networks; Prostate cancer; Support vector machines; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.836259
  • Filename
    836259