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• W2016683511 abstract "No AccessJournal of UrologyClinical Urology: Original Articles1 Aug 1998NOVEL STAGING TOOL FOR LOCALIZED PROSTATE CANCER: A PILOT STUDY USING GENETIC ADAPTIVE NEURAL NETWORKS ASHUTOSH TEWARI and PERINCHERY NARAYAN ASHUTOSH TEWARIASHUTOSH TEWARI More articles by this author and PERINCHERY NARAYANPERINCHERY NARAYAN More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(01)62916-1AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract Purpose: An estimated $1.5 billion is spent annually for direct medical expenses and an additional $2.5 billion for indirect costs for the management of prostate cancer. Today there are several procedures for staging prostate cancer, including lymph node dissection. Despite these procedures, the accuracy of predicting extracapsular disease remains low (range 37 to 63, mean 45%). Use of multiple staging procedures adds significantly to the costs of managing prostate cancer. Recently artificial intelligence based neural networks have become available for medical applications. Unlike traditional statistical methods, these networks do not assume linearity or homogeneity of variance and, thus, they are more accurate for clinical data. We applied this concept to staging localized prostate cancer and devised an algorithm that can be used for prostate cancer staging. Materials and Methods: Our study comprised 1,200 men with clinically organ confined prostate cancer who underwent preoperative staging using serum prostate specific antigen, systematic biopsy and Gleason scoring before radical prostatectomy and lymphadenectomy. The performance of the neural network was validated for a subset of patients and network predictions were compared with actual pathological stage. Mean patient age was 62.9 years, mean serum prostate specific antigen 8.1 ng./ml. and mean biopsy Gleason 6. Of the patients 55% had organ confined disease, 27% positive margins, 8% seminal vesicle involvement and 7% lymph node disease. Of margin positive patients 30% also had seminal vesicle involvement, while of seminal vesicle positive patients 50% also had positive margins. Results: The sensitivity of the network was 81 to 100%, and specificity was 72 to 75% for various predictions of margin, seminal vesicle and lymph node involvement. The negative predictive values tended to be relatively high for all 3 features (range 92 to 100%). The neural network missed only 8% of patients with margin positive disease, and 2% with lymph node and 0% with seminal vesicle involvement. Conclusions: Our study suggests that neural networks may be useful as an initial staging tool for detection of extracapsular extension in patients with clinically organ confined prostate cancer. 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Google Scholar Division of Urology, University of Florida and Department of Veteran Affairs Medical Center, Gainesville, Florida© 1998 by American Urological Association, Inc.FiguresReferencesRelatedDetailsCited byAbbod M, Catto J, Linkens D and Hamdy F (2007) Application of Artificial Intelligence to the Management of Urological CancerJournal of Urology, VOL. 178, NO. 4, (1150-1156), Online publication date: 1-Oct-2007.NAYA Y and BABAIAN R (2018) The Predictors of Pelvic Lymph Node Metastasis at Radical Retropubic ProstatectomyJournal of Urology, VOL. 170, NO. 6, (2306-2310), Online publication date: 1-Dec-2003.CONRAD S, GRAEFEN M, PICHLMEIER U, HENKE R, ERBERSDOBLER A, HAMMERER P and HULAND H (2018) PROSPECTIVE VALIDATION OF AN ALGORITHM WITH SYSTEMATIC SEXTANT BIOPSY TO PREDICT PELVIC LYMPH NODE METASTASIS IN PATIENTS WITH CLINICALLY LOCALIZED PROSTATIC CARCINOMAJournal of Urology, VOL. 167, NO. 2 Part 1, (521-525), Online publication date: 1-Feb-2002.BORQUE A, SANZ G, ALLEPUZ C, PLAZA L, GIL P and RIOJA L (2018) THE USE OF NEURAL NETWORKS AND LOGISTIC REGRESSION ANALYSIS FOR PREDICTING PATHOLOGICAL STAGE IN MEN UNDERGOING RADICAL PROSTATECTOMY: A POPULATION BASED STUDYJournal of Urology, VOL. 166, NO. 5, (1672-1678), Online publication date: 1-Nov-2001.SONKE G, HESKES T, VERBEEK A, DE LA ROSETTE J and KIEMENEY L (2018) PREDICTION OF BLADDER OUTLET OBSTRUCTION IN MEN WITH LOWER URINARY TRACT SYMPTOMS USING ARTIFICIAL NEURAL NETWORKSJournal of Urology, VOL. 163, NO. 1, (300-305), Online publication date: 1-Jan-2000. Volume 160Issue 2August 1998Page: 430-436 Advertisement Copyright & Permissions© 1998 by American Urological Association, Inc.MetricsAuthor Information ASHUTOSH TEWARI More articles by this author PERINCHERY NARAYAN More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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