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• W2148532893 abstract "No AccessJournal of UrologyCLINICAL UROLOGY: Original Articles1 Mar 2003Neural Network Using Combined Urine Nuclear Matrix Protein-22, Monocyte Chemoattractant Protein-1 and Urinary Intercellular Adhesion Molecule-1 to Detect Bladder Cancer SIJO J. PAREKATTIL, HUGH A.G. FISHER, and BARRY A. KOGAN SIJO J. PAREKATTILSIJO J. PAREKATTIL More articles by this author , HUGH A.G. FISHERHUGH A.G. FISHER More articles by this author , and BARRY A. KOGANBARRY A. KOGAN More articles by this author View All Author Informationhttps://doi.org/10.1097/01.ju.0000051322.60266.06AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract Purpose: We developed a neural network to identify patients with bladder cancer more effectively than hematuria and cytology. The algorithm is based on combined urine levels of nuclear matrix protein-22, monocyte chemoattractant protein-1 and urinary intercellular adhesion molecule-1. Materials and Methods: A randomized double-blinded study of voided urine from 253 patients undergoing outpatient cystoscopy was performed. Of the patients 27 had bladder cancer on biopsy and 5 had muscle invasion. Urine tumor markers were measured using sandwich-enzyme-linked immunosorbent assay kits. Urine from patients with bladder cancer on cystoscopy was compared to urine from controls with negative cystoscopy results. An algorithm was created with 3 sets of cutoff values modeled to be 100% sensitive for superficial bladder cancer, 100% specific for superficial cancer and 100% specific for muscle invasive cancer, respectively. We compared our model to hematuria and cytology. Results: For the hematuria dipstick test sensitivity, specificity, positive and negative predictive values were 92.6%, 51.8%, 18.7% and 98.2%, respectively. For atypical cytology sensitivity, specificity, positive and negative predictive values were 66.7%, 81%, 29.5% and 95.3%, respectively. For the sensitive model set sensitivity, specificity, positive and negative predictive values were 100%, 75.7%, 32.9% and 100%, respectively. For the specific model set sensitivity, specificity, positive and negative predictive values were 22.2%, 100%, 100% and 91.5%, respectively. For the muscle invasive model set sensitivity, specificity, positive and negative predictive values were 80%, 100%, 100% and 99.6%, respectively. The standard bladder tumor evaluation of 253 patients costs $61,054 but $36,450 using our model. Conclusions: Our algorithm is superior to conventional screening tests for bladder cancer. The model identifies patients who require cystoscopy, those with bladder cancer and those with muscle invasive disease. It provides possible savings over current screening methods. The potential loss of other information by not performing cystoscopy was not evaluated in our study. References 1 : NMP22 is a sensitive, cost-effective test in patients at risk for bladder cancer. J Urol1999; 161: 62. Link, Google Scholar 2 : Cancer statistics, 1996. CA Cancer J Clin1996; 46: 5. Google Scholar 3 : Comparative sensitivity of urinary CYFRA 21-1, urinary bladder cancer antigen, tissue polypeptide antigen, tissue polypeptide antigen and NMP22 to detect bladder cancer. J Urol1999; 162: 1951. Link, Google Scholar 4 : Potential value of urinary intercellular adhesion molecule-1 determination in patients with bladder cancer. Urology1998; 52: 1015. Google Scholar 5 : Urinary levels of monocyte chemo-attractant protein-1 correlate with tumour stage and grade in patients with bladder cancer. Br J Urol1998; 82: 118. Google Scholar 6 : Four bladder tumor markers have a disappointingly low sensitivity for small size and low grade recurrence. J Urol2002; 167: 80. Link, Google Scholar 7 : Artificial neural networks: opening the black box. Cancer2001; 91: 1615. Google Scholar 8 : Novel artificial neural network for early detection of prostate cancer. J Clin Oncol2002; 20: 921. Google Scholar 9 : Epidemiology, biostatistics, and preventive medicine. Philadelphia: W. B. Saunders Co.1996: IX. Google Scholar From the Division of Urology, Albany Medical Center, Albany, New York© 2003 by American Urological Association, Inc.FiguresReferencesRelatedDetailsCited byAbbod M, Catto J, Linkens D and Hamdy F (2018) Application of Artificial Intelligence to the Management of Urological CancerJournal of Urology, VOL. 178, NO. 4, (1150-1156), Online publication date: 1-Oct-2007.SAMLI M and DOGAN I (2018) AN ARTIFICIAL NEURAL NETWORK FOR PREDICTING THE PRESENCE OF SPERMATOZOA IN THE TESTES OF MEN WITH NONOBSTRUCTIVE AZOOSPERMIAJournal of Urology, VOL. 171, NO. 6 Part 1, (2354-2357), Online publication date: 1-Jun-2004. Volume 169Issue 3March 2003Page: 917-920 Advertisement Copyright & Permissions© 2003 by American Urological Association, Inc.Keywordsbladdercystoscopybladder neoplasmsalgorithmstumor markers, biologicalMetricsAuthor Information SIJO J. PAREKATTIL More articles by this author HUGH A.G. FISHER More articles by this author BARRY A. KOGAN More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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• W2148532893 title "Neural Network Using Combined Urine Nuclear Matrix Protein-22, Monocyte Chemoattractant Protein-1 and Urinary Intercellular Adhesion Molecule-1 to Detect Bladder Cancer" @default.
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