Artificial Neural Networks in the Diagnosis and Prognosis of Prostate Cancer: A Pilot Study

No AccessJournal of Urology1 Nov 1994Artificial Neural Networks in the Diagnosis and Prognosis of Prostate Cancer: A Pilot Study Peter B. Snow, Deborah S. Smith, and William J. Catalona Peter B. SnowPeter B. Snow , Deborah S. SmithDeborah S. Smith , and William J. CatalonaWilliam J. Catalona View All Author Informationhttps://doi.org/10.1016/S0022-5347(17)32416-3AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail There is controversy about how prostate cancer screening tests should best be used because of the false-negative and false-positive results. There also is controversy about prostate cancer treatment because of errors in tumor staging, uncertainty about treatment efficacy and the variable natural history of the disease. We sought to determine in a pilot study whether artificial neural networks would be helpful to predict biopsy results in men with abnormal screening test(s) and to predict treatment outcome after radical prostatectomy. To predict biopsy results, we extracted data from a prostate specific antigen (PSA) based screening study data base in 1,787 men with a serum PSA concentration of more than 4.0 ng./ml. (approximately 40% of the men also had suspicious findings on digital rectal examination). To predict cancer recurrence after radical prostatectomy, we extracted data from a random sample of 240 patients selected from a data base of men who had undergone radical prostatectomy. The neural network predicted the biopsy result with 87% overall accuracy, and its output threshold could be adjusted to achieve the desired tradeoff between sensitivity and specificity. It also predicted tumor recurrence with 90% overall accuracy. We conclude that trained neural networks may be useful in decision making for prostate cancer patients. © 1994 by The American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsCited ByTaneja S (2020) Re: Artificial Intelligence for Diagnosis and Grading of Prostate Cancer in Biopsies: A Population-Based, Diagnostic StudyJournal of Urology, VOL. 204, NO. 3, (620-621), Online publication date: 1-Sep-2020.Thompson R, Blute M, Slezak J, Bergstralh E and Leibovich B (2007) Is the GPSM Scoring Algorithm for Patients With Prostate Cancer Valid in the Contemporary Era?Journal of Urology, VOL. 178, NO. 2, (459-463), Online publication date: 1-Aug-2007.GRAEFEN M, OHORI M, KARAKIEWICZ P, CAGIANNOS I, HAMMERER P, HAESE A, ERBERSDOBLER A, HENKE R, HULAND H, WHEELER T, SLAWIN K, SCARDINO P and KATTAN M (2018) Assessment of the Enhancement in Predictive Accuracy Provided by Systematic Biopsy in Predicting Outcome for Clinically Localized Prostate CancerJournal of Urology, VOL. 171, NO. 1, (200-203), Online publication date: 1-Jan-2004.ZLOTTA A, REMZI M, SNOW P, SCHULMAN C, MARBERGER M and DJAVAN B (2018) An Artificial Neural Network for Prostate Cancer Staging when Serum Prostate Specific Antigen is 10 NG./ML. or LessJournal of Urology, VOL. 169, NO. 5, (1724-1728), Online publication date: 1-May-2003.WADIE B, BADAWI A and GHONEIM M (2018) THE RELATIONSHIP OF THE INTERNATIONAL PROSTATE SYMPTOM SCORE AND OBJECTIVE PARAMETERS FOR DIAGNOSING BLADDER OUTLET OBSTRUCTION. PART II: THE POTENTIAL USEFULNESS OF ARTIFICIAL NEURAL NETWORKSJournal of Urology, VOL. 165, NO. 1, (35-37), Online publication date: 1-Jan-2001.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.ROSS P, SCARDINO P and KATTAN M (2018) A CATALOG OF PROSTATE CANCER NOMOGRAMSJournal of Urology, VOL. 165, NO. 5, (1562-1568), Online publication date: 1-May-2001.QURESHI K, NAGUIB R, HAMDY F, NEAL D and MELLON J (2018) NEURAL NETWORK ANALYSIS OF CLINICOPATHOLOGICAL AND MOLECULAR MARKERS IN BLADDER CANCERJournal of Urology, VOL. 163, NO. 2, (630-633), Online publication date: 1-Feb-2000.TEWARI A and NARAYAN P (2018) NOVEL STAGING TOOL FOR LOCALIZED PROSTATE CANCER: A PILOT STUDY USING GENETIC ADAPTIVE NEURAL NETWORKSJournal of Urology, VOL. 160, NO. 2, (430-436), Online publication date: 1-Aug-1998.Krongrad A, Granville L, Burke M, Golden R, Lai S, Cho L and Niederberger C (2018) Predictors of General Quality of Life in Patients With Benign Prostate Hyperplasia or Prostate CancerJournal of Urology, VOL. 157, NO. 2, (534-538), Online publication date: 1-Feb-1997.Niederberger C (2018) Commentary on the Use of Neural Networks in Clinical UrologyJournal of Urology, VOL. 153, NO. 5, (1362-1362), Online publication date: 1-May-1995.Lange P (2018) Future studies in localized prostate cancer. What should we think? What can we do?Journal of Urology, VOL. 152, NO. 5 Part 2, (1932-1938), Online publication date: 1-Nov-1994. Volume 152Issue 5 Part 2November 1994Page: 1923-1926 Advertisement Copyright & Permissions© 1994 by The American Urological Association Education and Research, Inc.Keywordsneoplasmcomputerantigensneural networksoutcome assessment (health care)prostatic neoplasmsMetricsAuthor Information Peter B. Snow More articles by this author Deborah S. Smith More articles by this author William J. Catalona More articles by this author Expand All Advertisement Loading ...