Objective: The choice of therapy for prostatic cancer should depend on a rational preoperative estimate of tumor stage. Artificial neural networks were used to predict postoperative staging of prostatic cancer from sextant biopsies and routinely available preoperative data. Methods: In group I (97 cases), nonorgan confinement (tumor stage ≧pT3a) was predicted on the basis of age and six histopathological variables from sextant biopsies. In group II (77 cases), nonorgan confinement and extraprostatic organ infiltration (tumor classification ≧pT3b) were predicted from age, four histopathological variables, the preoperative PSA level, and the total prostate volume estimated by preoperative ultrasonography. Learning vector quantization (LVQ) networks were applied for this purpose and compared to multilayer perceptrons (MLP) and linear discriminant analysis (LDA). Results: Nonorgan confinement could be predicted correctly in 90% of newly presented cases from sextant biopsy histopathology alone. A similar accuracy of predicting nonorgan confinement (83%) was obtained by combining preoperative biopsy histology with clinical data. Extraprostatic organ infiltration could be predicted correctly in 82%. The best results were obtained by LVQ networks, followed by MLP networks and LDA. Conclusion: The postoperative tumor stage of prostatic cancer can be estimated with high accuracy, sensitivity and specificity from preoperative routine parameters using artificial neural networks, especially LVQ networks. The results suggest that this methodology should be evaluated in a larger prospective study.

Garnick MB: Prostate cancer: Screening, diagnosis, and management. Ann Intern Med 1993; 118:804–818.
Waterbor JW, Bueschen AJ: Prostate cancer screening (United States). Cancer Causes Control 1995;6:267–274.
Johansson J–E, Adami H–O, Andersson S–O, Bergström R, Holmberg L, Krusemo UB: High 10–year survival in patients with early, untreated prostatic cancer. JAMA 1992;267:2191– 2196.
Chodak GW, Thisted RA, Gerber GS, Johansson JE, Adolfsson J, Jones GW, Chisholm GD, Moskovitz B, Livne P, Warner J: Results of conservative management of clinically localized prostate cancer. N Engl J Med 1994; 330:242–248.
Gittes RF: Carcinoma of the prostate. N Engl J Med 1991;324:236–245.
Narayan P, Tewari A: Systematic biopsy–based staging of prostate cancer: Scientific background, individual variables, combination of parameters, and current integrative models. Semin Urol Oncol 1998;16:172–181.
Bates TS, Gillatt DA, Cavanagh PM, Speakman M: A comparison of endorectal magnetic resonance imaging and transrectal ultrasound in the local staging of prostate cancer with histopathological correlation. Br J Urol 1997;79:927–932.
Snow PB, Smith DS, Catalona WJ: Artificial neural networks in the diagnosis and prognosis of prostate cancer: A pilot study. J Urol 1994; 152:1923–1926.
Miles BJ, Kattan MW: Computer modeling of prostate cancer treatment. A paradigm for oncologic management? Adv Urol Oncol 1995; 4:361–372.
Stotzka R, Männer R, Bartels PH, Thompson D: A hybrid neural and statistical classifier system for histopathologic grading of prostatic lesions. Anal Quant Cytol Histol 1995;17:204– 218.
Montironi R, Bartels PH, Thompson D, Diamanti L, Prete E: Androgen–deprived prostate adenocarcinoma: Evaluation of treatment–related changes versus no distinctive treatment effect with a Bayesian belief network. Eur Urol 1996;30:307–315.
Krongrad A, Granville LJ, Burke MA, Golden RM, Lai S, Cho L, Niederberger CS: Predictors of general quality of life in patients with benign prostate hyperplasia or prostate cancer. J Urol 1997;157:534–538.
Tewari A: Artificial intelligence and neural networks: Concept, applications and future in oncology. Br J Urol 1997;80(suppl 3):53–58.
Bostwick DG: Practical clinical application of predictive factors in prostate cancer. A review with an emphasis on quantitative methods in tissue specimens. Anal Quant Cytol Histol 1998;20:323–342.
Douglas TH, Moul JW: Applications of neural networks in urologic oncology. Semin Urol Oncol 1998;16:35–39.
Naguib RNG, Robinson MC, Neal DE, Hamdy FC: Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: A pilot study. Br J Cancer 1998;78:246–250.
Tewari A, Narayan P: Novel staging tool for localized prostatic cancer: A pilot study using genetic adaptive neural networks. J Urol 1998;160:430–436.
Wei JT, Zhang Z, Barnhill SD, Madyastha KR, Zhang H, Oesterling JE: Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology 1998;52:161–172.
Mattfeldt T, Kestler HA, Hautmann R, Gottfried H–W: Prediction of prostatic cancer progression after radical prostatectomy using artificial neural networks: A feasibility study. BJU Int 1999;84:316–323.
Mattfeldt T, Gottfried H–W, Schmidt V, Kestler HA: Classification of spatial textures in benign and cancerous glandular tissues by stereology and stochastic geometry using artifical neural networks. J Microsc 2000;198:143–158.
Hodge KK, McNeal JE, Terris MK, Stamey TA: Random systematic versus directed ultrasound guided transrectal core biopsies of the prostate. J Urol 1989;142:71–74.
Sobin LH, Wittekind C (eds): TNM Classification of Malignant Tumours, ed 5. New York, Wiley, 1997, pp 170–173.
Tourassi GD, Floyd CE: The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. Med Decis Making 1997;17:186–192.
Henderson AR: Assessing test accuracy and its clinical consequences: A primer for receiver operating characteristic curve analysis. Ann Clin Biochem 1993;30:521–539.
Zell A: Simulation neuronaler Netze. Bonn, Addison–Wesley, 1994.
Kohonen T: Self–Organizing Maps, ed 2. Heidelberg, Springer, 1997.
Pantazopoulos D, Karakitsos P, Iokim–Liossi A, Pouliakis A, Dimopoulos K: Comparing neural networks in the discrimination of benign from malignant lower urinary tract lesions. Br J Urol 1998;81:574–579.
Pantazopoulos D, Karakitsos P, Pouliakis A, Iokim–Liossi A, Dimopoulos MA: Static cytometry and neural networks in the discrimination of lower urinary system lesions. Urology 1998;51:946–950.
Lawrence S, Burns I, Back A, Tsoi AC, Giles CL: Neural network classification and prior class probabilities; in Orr GB, Müller K–R (eds): Neural Networks: Tricks of the Trade. Berlin, Springer, 1998, pp 299–313.
Gleason DF: Histologic grading of prostate cancer: A perspective. Hum Pathol 1992;23: 273–279.
Garnick JE, Oyasu R, Grayhack JT: The accuracy of diagnostic biopsy specimens in predicting tumor grades by Gleason’s classification of radical prostatectomy specimens. J Urol 1984; 131:690–693.
Mills SE, Fowler JE: Gleason histologic grading of prostatic carcinoma. Correlations between biopsy and prostatectomy specimens. Cancer 1986;57:346–349.
Lang TA, Secic M: How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Reviewers. Philadelphia, American College of Physicians/BMJ Publishing Group, 1997.
SAS Institute: SAS/STAT User’s Guide, Release 6.03 Edition. Cary, SAS Institute, 1988.
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.