title: Back propagation neural network in the discrimination of benign from malignant lower urinary tract lesions.
abstract: OBJECTIVE
  We investigated the potential value of morphometry and artificial intelligence tools to discriminate between benign and malignant lower urinary tract lesions.
  
  
  METHODS
  The lesions included lithiasis in 50 cases, inflammation in 61, benign prostatic hyperplasia in 99, carcinoma in situ in 5, and grade I and grades II and III transitional cell carcinoma of the bladder in 71 and 184, respectively. Images of routine processed voided urine smears stained by the Giemsa technique were analyzed using a custom image analysis system, providing a data set of 45,452 cells. A neural net model of the back propagation type was used to discriminate benign from malignant cells based on the extracted morphometric and textural features. Data from 13,636 randomly selected cells (30% of the total data) were used as a training set and the data from the remaining 31,816 cells comprised the test set. In a similar attempt to discriminate at the patient level data on 30% of those randomly selected were used to train a back propagation neural net and data on the remaining 329 were used for testing.
  
  
  RESULTS
  Application of the back propagation neural net enabled the correct classification of 95.34% of benign and 86.71% of malignant cells with overall 90.57% accuracy. At the patient level the back propagation neural net enabled the correct classification of 100% of those with benign and 94.51% of those with malignant disease with overall 96.96% accuracy.
  
  
  CONCLUSIONS
  The use of neural nets and image morphometry may increase the speed of cytological diagnosis and the diagnostic accuracy of voided urine cytology.
authors.affiliation: Department of Urology, University of Athens, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Pantazopoulos
authors.forename: P
authors.initials: P
authors.lastname: Karakitsos
authors.forename: A
authors.initials: A
authors.lastname: Iokim-Liossi
authors.forename: A
authors.initials: A
authors.lastname: Pouliakis
authors.forename: E
authors.initials: E
authors.lastname: Botsoli-Stergiou
authors.forename: C
authors.initials: C
authors.lastname: Dimopoulos
mesh_terms: D002278:Carcinoma in Situ / Q000175:diagnosis / Q000473:pathology; D002295:Carcinoma, Transitional Cell / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011470:Prostatic Hyperplasia / Q000175:diagnosis / Q000473:pathology; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis* / Q000473:pathology; D014545:Urinary Calculi / Q000175:diagnosis; D014570:Urologic Diseases / Q000175:diagnosis* / Q000473:pathology