title: A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence.
abstract: OBJECTIVE
  A computer-based system was designed, incorporating subjective criteria employed by pathologists in their usual microscopic observation of tissue samples and measurements of nuclear characteristics, with the purpose of automatically assessing urinary bladder tumour grade and predicting cancer recurrence.
  
  
  METHODS
  Ninety-two cases with urine bladder carcinoma were diagnosed and followed-up. Forty-seven patients had cancer recurrence. Each case was represented by eight histological (subjective) features, evaluated by pathologists, and thirty-six automatically extracted nuclear features. Grading and prognosis were performed by neural-network based classifiers employing both histological and nuclear features.
  
  
  RESULTS
  Employing a combination of histological and nuclear features, highest classification accuracy was 82%, 80.5%, and 93.1% for tumours of grade I, II and III respectively. The prognostic-system, gave a significant prognostic assessment of 72.8% with a confidence of 74.5% that cancer might recur and of 71.1% that might not, employing two histological features and two textural nuclear features.
  
  
  CONCLUSIONS
  The system for grading and predicting tumour recurrence may serve as a second opinion tool and features employed for designing the system may be of value to pathologists using descriptive grading systems.
authors.affiliation: Computer Laboratory, School of Medicine, University of Patras, Pio, Patras 265 00, Greece.
authors.forename: P
authors.initials: P
authors.lastname: Spyridonos
authors.forename: D
authors.initials: D
authors.lastname: Cavouras
authors.forename: P
authors.initials: P
authors.lastname: Ravazoula
authors.forename: G
authors.initials: G
authors.lastname: Nikiforidis
mesh_terms: D020000:Decision Support Systems, Clinical; D006115:Greece; D006801:Humans; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D010338:Pathology, Clinical / Q000295:instrumentation* / Q000379:methods; D011379:Prognosis; D001749:Urinary Bladder Neoplasms / Q000145:classification / Q000175:diagnosis*