Novel staging tool for localized prostate cancer: a pilot study using genetic adaptive neural networks.

[1]  A. D'Amico,et al.  Outcome based staging for clinically localized adenocarcinoma of the prostate. , 1997, The Journal of urology.

[2]  J. Oesterling,et al.  A nationwide survey of practicing urologists: current management of benign prostatic hyperplasia and clinically localized prostate cancer. , 1997, The Journal of urology.

[3]  H. Lai,et al.  Survival after radical prostatectomy. , 1997, JAMA.

[4]  M. Kattan,et al.  Has there been a recent shift in the pathological features and prognosis of patients treated with radical prostatectomy? , 1997, The Journal of urology.

[5]  G. Miller,et al.  Artificial Evolution: A New Path for Artificial Intelligence? , 1997, Brain and Cognition.

[6]  A W Partin,et al.  Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. , 1997, JAMA.

[7]  M F Jefferson,et al.  Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma , 1997, Cancer.

[8]  R. Orr,et al.  Use of a Probabilistic Neural Network to Estimate the Risk of Mortality after Cardiac Surgery , 1997, Medical decision making : an international journal of the Society for Medical Decision Making.

[9]  A. D'Amico,et al.  Combined modality staging of prostate carcinoma and its utility in predicting pathologic stage and postoperative prostate specific antigen failure. , 1997, Urology.

[10]  F. Harrell,et al.  Artificial neural networks improve the accuracy of cancer survival prediction , 1997, Cancer.

[11]  M W Kattan,et al.  Evaluation of a Nomogram used to predict the pathologic stage of clinically localized prostate carcinoma , 1997, Cancer.

[12]  J. Epstein.  Incidence and significance of positive margins in radical prostatectomy specimens. , 1996, The Urologic clinics of North America.

[13]  P. Unger,et al.  Seminal vesicle biopsy: accuracy and implications for staging of prostate cancer. , 1996, Urology.

[14]  M E Brier,et al.  Application of artificial neural networks to clinical pharmacology. , 1996, International journal of clinical pharmacology and therapeutics.

[15]  D C Young,et al.  An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level. , 1996, The Journal of urology.

[16]  W. Murphy.  Prostate cancer. The problem of prognostic factors. , 1996, American journal of clinical pathology.

[17]  C. Niederberger.  Computational tools for the modern andrologist. , 1996, Journal of andrology.

[18]  C. Tempany.  MR staging of prostate cancer. How we can improve our accuracy with decisions aids and optimal techniques. , 1996, Magnetic resonance imaging clinics of North America.

[19]  Igor V. Tetko,et al.  Neural Network Studies, 2. Variable Selection , 1996, J. Chem. Inf. Comput. Sci..

[20]  R. Ho,et al.  The National Cancer Data Base report on longitudinal observations on prostate cancer , 1996, Cancer.

[21]  M. Blute,et al.  Risk factors for progression in patients with prostate cancer treated with radical prostatectomy. , 1996, Seminars in urologic oncology.

[22]  D. Bostwick,et al.  Prediction of capsular perforation and seminal vesicle invasion in prostate cancer. , 1996, The Journal of urology.

[23]  A. Partin,et al.  Prediction of progression following radical prostatectomy. A multivariate analysis of 721 men with long-term follow-up. , 1996, The American journal of surgical pathology.

[24]  M. Menon,et al.  Should we treat localized prostate cancer? An opinion. , 1995, Urology.

[25]  Yoshikane Takahashi,et al.  A mathematical solution to a Network Designing Problem , 1995, Neurocomputing.

[26]  K. J. Dalton,et al.  Artificial neural networks for decision support in clinical medicine. , 1995, Annals of medicine.

[27]  J. Oesterling,et al.  Using prostate-specific antigen to eliminate unnecessary diagnostic tests: significant worldwide economic implications. , 1995, Urology.

[28]  Peter G. Korning,et al.  Training neural networks by means of genetic algorithms working on very long chromosomes , 1995, Int. J. Neural Syst..

[29]  A. Tewari,et al.  The role of transrectal ultrasound-guided biopsy-based staging, preoperative serum prostate-specific antigen, and biopsy Gleason score in prediction of final pathologic diagnosis in prostate cancer. , 1995, Urology.

[30]  H. Zincke,et al.  Potency-Sparing Radical Retropubic Prostatectomy: A Simplified Anatomical Approach , 1995 .

[31]  P H Bartels,et al.  Clinical applications of Bayesian belief networks in pathology. , 1995, Pathologica.

[32]  Milestone Bn,et al.  Endorectal coil magnetic resonance imaging of prostate cancer. , 1995 .

[33]  M Levin,et al.  Use of genetic algorithms to solve biomedical problems. , 1995, M.D. computing : computers in medical practice.

[34]  C. Niederberger.  This month in Investigative Urology. Commentary on the use of neural networks in clinical urology. , 1995, The Journal of urology.

[35]  P. Carroll,et al.  The use and accuracy of cross-sectional imaging and fine needle aspiration cytology for detection of pelvic lymph node metastases before radical prostatectomy. , 1995, The Journal of urology.

[36]  P. Carroll,et al.  Original Articles: Prostate Cancer: The Use and Accuracy of Cross-Sectional Imaging and Fine Needle Aspiration Cytology for Detection of Pelvic Lymph Node Metastases Before Radical Prostatectomy , 1995 .

[37]  W. Catalona,et al.  Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. , 1994, The Journal of urology.

[38]  J. Oesterling,et al.  Long-term (15 years) results after radical prostatectomy for clinically localized (stage T2c or lower) prostate cancer. , 1994, The Journal of urology.

[39]  P. Walsh.  Editorial: The Status of Radical Prostatectomy in the United States in 1993: Where do we go from here? , 1994 .

[40]  T. Stamey,et al.  The pathological features and prognosis of prostate cancer detectable with current diagnostic tests. , 1994, The Journal of urology.

[41]  W. Dassen,et al.  The Value of Artificial Neural Network Techniques to Develop Diagnostic Systems in Cardiology , 1994, Pacing and clinical electrophysiology : PACE.

[42]  P. Narayan,et al.  Utility of preoperative serumprostate-specific antigen concentration and biopsy Gleason score in predicting risk of pelvic lymph node metastases in prostate cancer , 1994 .

[43]  B P Bergeron,et al.  Data qualification: logic analysis applied toward neural network training. , 1994, Computers in biology and medicine.

[44]  E. Jakobsen,et al.  Neural Network for Automatic Analysis of Motility Data , 1994, Methods of Information in Medicine.

[45]  J. Oesterling,et al.  Using PSA to eliminate the staging radionuclide bone scan. Significant economic implications. , 1993, The Urologic clinics of North America.

[46]  R. Kane,et al.  Characteristics of prostate cancers detected in a multimodality early detection program , 1993 .

[47]  Aristides T. Hatjimihait,et al.  Genetic algorithms-based design and optimization of statistical quality-control procedures. , 1993, Clinical chemistry.

[48]  S Forrest,et al.  Genetic algorithms , 1996, CSUR.

[49]  D. Lamb,et al.  A neural network to analyze fertility data. , 1993, Fertility and sterility.

[50]  D. Chan,et al.  The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. , 1993, The Journal of urology.

[51]  P. Walsh,et al.  Influence of capsular penetration on progression following radical prostatectomy: a study of 196 cases with long-term followup. , 1993, The Journal of urology.

[52]  Galina Pizov,et al.  Correlation of pathologic findings with progression after radical retropubic prostatectomy , 1993, Cancer.

[53]  D. Bostwick.  Staging prostate cancer--1997: current methods and limitations. , 1997, European urology.

[54]  M. D. Williams,et al.  Neural networks: what are they? , 1997, The Journal of burn care & rehabilitation.

[55]  M. Menon.  Predicting biological aggressiveness in prostate cancer--desperately seeking a marker. , 1997, The Journal of urology.

[56]  N. Lefaucheur.  Qui doit nourrir l'enfant de parents non mariés ou démariés ? : La charge de l'enfant , 1997 .

[57]  P. Wingo,et al.  Cancer statistics, 1997 , 1997, CA: a cancer journal for clinicians.

[58]  Igor V. Tetko,et al.  Data modelling with neural networks: Advantages and limitations , 1997, J. Comput. Aided Mol. Des..

[59]  M Buscema,et al.  A general presentation of artificial neural networks. I. , 1997, Substance use & misuse.

[60]  M. Cowen,et al.  Computer modeling in urology. , 1996, Urology.

[61]  P. Walsh.  Re: Potency-sparing radical retropubic prostatectomy: a simplified anatomical approach. , 1996, The Journal of urology.

[62]  J. M. Minor,et al.  Analysis of clinical data using neural nets. , 1996, Journal of biopharmaceutical statistics.

[63]  F Alemi,et al.  A comparison of three techniques for rapid model development: an application in patient risk-stratification. , 1996, Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium.

[64]  D. Fogel,et al.  A comparison of methods for self-adaptation in evolutionary algorithms. , 1995, Bio Systems.

[65]  R M Borisyuk,et al.  Dynamics and bifurcations of two coupled neural oscillators with different connection types , 1995, Bulletin of mathematical biology.

[66]  W. Demark-Wahnefried,et al.  The National Cancer Data Base report on prostate cancer. , 1995, Cancer.

[67]  H. Burke,et al.  Artificial neural networks for cancer research: outcome prediction. , 1994, Seminars in surgical oncology.

[68]  A. Partin,et al.  Treatment of early stage prostate cancer: radical prostatectomy. , 1994, Important advances in oncology.