45
Views
7
CrossRef citations to date
0
Altmetric
Original Article

Epidemiology and statistical methods in prediction of patient outcome

, , , , , & show all
Pages 94-110 | Published online: 09 Jul 2009

References

  • Jemal A, Tiwari RC, Murray T, etal. Cancer statistics, 2004. CA Cancer J Clin 2004;54:8–29.
  • Sakr WA, Haas GP, Cassin BF, et al. The frequency of carcinoma and intraepithelial neoplasia of the prostate in young male patients. J Urol 1993;150:379–85.
  • Burke HB. Artificial neural networks for cancer research: outcome prediction. Semin Surg Oncol 1994;10:73–9.
  • Burke HB. Statistical analysis of complex systems in biomedicine. New York: Springer-Verlag; 1996. p. 251–8.
  • Burke HB. Risk assessment. Atlanta, GA: American Cancer Society; 2004.
  • Thompson IM, Pauler DK, Goodman PJ, et al. Prevalence of prostate cancer among men with a prostate-specific antigen level or =4.0 ng per milliliter. N Engl J Med 2004;350:2239–46.
  • Bostwick DG, Burke HB, Djakiew D, et al. Human prostate cancer risk factors. Cancer 2004;101(10 Suppl):2371–490.
  • Breslow N, Chan CW, Dhom G, et al. Latent carcinoma of prostate at autopsy in seven areas. The International Agency for Research on Cancer, Lyons, France. Int J Cancer 1977;20:680–8.
  • Carter BS, Carter HB, Isaacs JT. Epidemiologic evidence regarding predisposing factors to prostate cancer. Prostate 1990;16:187–97.
  • Oishi K, Yoshida 0, Schroeder FH. The geography of prostate cancer and its treatment in Japan. Cancer Surv 1995;23:267–80.
  • Parkin DM, Muir CS.Cancer incidence in five continents. Comparability and quality of data. IARC Sci Publ 1992:45–173.
  • Morrison HI, MacNeill IB, Miller D, et al. The impending Canadian prostate cancer epidemic. Can J Publ Health 1995;86:274–8.
  • Doherty AP, Christmas TJ. Diagnosis and treatment of urological malignancy: the prostate. Br J Hosp Med 1996;55:104-6, 123–4.
  • Dijkman GA, Debruyne FM. Epidemiology of prostate cancer. Eur Urol 1996;30:281–95.
  • Wingo PA, Ries LA, Rosenberg HM, etal. Cancer incidence and mortality, 1973-1995: a report card for the U.S. Cancer 1998;82:1197–207.
  • Wingo PA, Ries LA, Parker SL, et al. Long-term cancer patient survival in the United States. Cancer Epidemiol Biomarkers Prey 1998;7:271–82.
  • Devesa SS, Blot WJ, Stone BJ, etal. Recent cancer trends in the United States. J Natl Cancer Inst 1995;87:175–82.
  • Merrill RNI, Potosky AL, Feuer EJ. Changing trends in U.S. prostate cancer incidence rates. J Natl Cancer Inst 1996;88:1683–5.
  • Merrill RNI, Lyon JL. Explaining the difference in prostate cancer mortality rates between white and black men in the United States. Urology 2000;55:730–5.
  • Mettlin C, Murphy GP, Babaian RJ, et al. The results of a five-year early prostate cancer detection intervention. In-vestigators of the American Cancer Society National Pros-tate Cancer Detection Project. Cancer 1996;77:150–9.
  • Brawley OW. Prostate carcinoma incidence and patient mortality: the effects of screening and early detection. Cancer 1997;80:1857–63.
  • Potosky AL, Kessler L, Gridley G, et al. Rise in prostatic cancer incidence associated with increased use of transure-thral resection. J Natl Cancer Inst 1990;82:1624–8.
  • Levy IG, Gibbons L, Collins JP, et al. Prostate cancer trends in Canada: rising incidence or increased detection? CMAJ 1993;149:617–24.
  • Smart CR, Byrne C, Smith RA, et al. Twenty-year follow-up of the breast cancers diagnosed during the Breast Cancer Detection Demonstration Project. CA Cancer J Clin 1997;47:134–49.
  • Zaridze DG, Boyle P. Cancer of the prostate: epidemiology and aetiology. Br J Urol 1987;59:493–502.
  • Pienta KJ, Demers R, Hoff M, et al. Effect of age and race on the survival of men with prostate cancer in the Metropolitan Detroit tricounty area, 1973 to 1987. Urology 1995;45:93-101; discussion 101–2.
  • Dhom G. Epidemiologic aspects of latent and clinically manifest carcinoma of the prostate. J Cancer Res Clin Oncol 1983;106:210–8.
  • Polednak AP. Trends in prostate carcinoma incidence in Connecticut (1988-1994) by age and race. Cancer 1997;79:99–103.
  • Polednak AP. Stage at diagnosis of prostate cancer in Connecticut by poverty and race. Ethn Dis 1997;7:215–20.
  • Waterbor JW, Bueschen AJ. Prostate cancer screening (United States). Cancer Causes Control 1995;6:267–74.
  • Burke HB. Prognosis. Atlanta, GA: American Cancer Society; 2004.
  • Ross RK. Epidemiology of prostate cancer and bladder cancer: an overview. Cancer Treat Res 1996;88:1–11.
  • Higgins IT. The epidemiology of cancer of the prostate. J Chronic Dis 1975;28:343.
  • Newman J.Epidemiology, diagnosis and treatment of pros-tate cancer. Radiol Technol 1996;68:39-64; quiz 65–8.
  • Bostwick DG, Burke HB. Prediction of individual patient outcome in cancer: comparison of artificial neural networks and Kaplan-Meier methods. Cancer 2001;91:1643–6.
  • Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 1997;79:857–62.
  • Partin AW, Kattan MW, Subong EN, et al. Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. JAMA 1997;277:1445–51.
  • D'Amico AV, Moul JW, Carroll PR, et al. Surrogate end point for prostate cancer-specific mortality after radical prostatectomy or radiation therapy. J Natl Cancer Inst 2003;95:1376–83.
  • D'Amico AV, Moul J, Carroll PR, et al. Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era. J Clin Oncol 2003;21:2163–72.
  • D'Amico AV, Whittington R, Malkowicz SB, et al. Bio-chemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clini-cally localized prostate cancer. JANIA 1998;280:969–74.
  • Di Blasio CJ, Rhee AC, Cho D, et al. Predicting clinical end points: treatment nomograms in prostate cancer. Semin Oncol 2003;30:567–86.
  • Kattan M. Statistical prediction models, artificial neural networks, and the sophism "I am a patient, not a statistic". J Clin Oncol 2002;20:885–7.
  • Kattan MW. Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preo-perative application in prostate cancer. Curr Opin Urol 2003;13:111–6.
  • Kattan MW, Eastham J. Algorithms for prostate-specific antigen recurrence after treatment of localized prostate cancer. Clin Prostate Cancer 2003;1:221–6.
  • Kattan MW, Eastham JA, Stapleton AM, et al. A preopera-tive nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 1998;90:766–71.
  • Kattan MW, Eastham JA, Wheeler TM, et al. Counseling men with prostate cancer: a nomogram for predicting the presence of small, moderately differentiated, confined tu-mors. J Urol 2003;170:1792–7.
  • Kattan MW, Potters L, Blasko JC, et al. Pretreatment nomogram for predicting freedom from recurrence after permanent prostate brachytherapy in prostate cancer. Urol-ogy 2001;58:393–9.
  • Kattan MW, Scardino PT. Prediction of progression: nomo-grams of clinical utility. Clin Prostate Cancer 2002;1:90–6.
  • Kattan MW, Stapleton AM, Wheeler TM, et al. Evaluation of a nomogram used to predict the pathologic stage of clinically localized prostate carcinoma. Cancer 1997;79:528–37.
  • Kattan MW, Wheeler TM, Scardino PT. Postoperative nomogram for disease recurrence after radical prostatectomy for prostate cancer. J Clin Oncol 1999;17:1499–507.
  • Kattan MW, Zelefsky MJ, Kupelian PA, et al. Pretreatment nomogram that predicts 5-year probability of metastasis following three-dimensional conformal radiation therapy for localized prostate cancer. J Clin Oncol 2003;21:4568–71.
  • Kattan MW, Zelefsky MJ, Kupelian PA, et al. Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer. J Clin Oncol 2000;18:3352–9.
  • Ross PL, Gerigk C, Gonen M, et al. Comparisons of nomograms and urologists' predictions in prostate cancer. Semin Urol Oncol 2002;20:82–8.
  • Ross PL, Scardino PT, Kattan MW. A catalog of prostate cancer nomograms. J Urol 2001;165:1562–8.
  • Burke HB, Bostwick DG. ProstAsure Index in the detection of prostate cancer. Urology 1998;52:531–2.
  • Burke HB, Hoang A, Iglehart JD, et al. Predicting response to adjuvant and radiation therapy in patients with early stage breast carcinoma. Cancer 1998;82:874–7.
  • Leshno M, Lin VY, Pinkis A, et al. Multilayer feedforward networks with nonpolynomial activation function can ap-proximate any function. Neural Networks 1993;6:861–7.
  • Hornik K, Stinchcombe M, White H. Multilayer feedfor-ward networks are universal approximators. Neural Net-works 1989;2:359–66.
  • Aprikian AG, Omar EA, Behlouli H. Artificial neural network analysis and the relationship of P53 and P21 (waf-1) protein expression with prognosis of advanced stage prostate cancer treated by androgen ablation. Prostate Cancer Prostatic Dis 1999;2: 53.
  • Babaian RJ, Fritsche H, Ayala A, et al. Performance of a neural network in detecting prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/mL. Urology 2000;56:1000–6.
  • Batuello JT, Gamito EJ, Crawford ED, et al. Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer. Urology 2001;57:481–5.
  • Borque A, Sanz G, Allepuz C, et al. The use of neural networks and logistic regression analysis for predicting pathological stage in men undergoing radical prostatectomy: a population based study. J Urol 2001;166:1672–8.
  • Crawford ED. Use of algorithms as determinants for individual patient decision making: national comprehensive cancer network versus artificial neural networks. Urology 2003;62:13–9.
  • Crawford ED, Batuello IF, Snow P, et al. The use of artificial intelligence technology to predict lymph node spread in men with clinically localized prostate carcinoma. Cancer 2000;88:2105–9.
  • Djavan B, Remzi M, Zlotta A, et al. Novel artificial neural network for early detection of prostate cancer. J Clin Oncol 2002;20:921 —9.
  • Douglas TH, Moul JW. Applications of neural networks in urologic oncology. Semin Urol Oncol 1998;16:35–9.
  • Finne P, Finne R, Auvinen A, et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 2000;56:418–22.
  • Han M, Snow PB, Brandt JM, et al. Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma. Cancer 2001;91:1661–6.
  • Han M, Snow PB, Epstein JI, et al. A neural network predicts progression for men with gleason score 3+4 versus 4+3 tumors after radical prostatectomy. Urology 2000;56:994–9.
  • Kalra P, Togami J, Bansal BSG, et al. A neurocomputational model for prostate carcinoma detection. Cancer 2003;98:1849 —54.
  • Matsui Y, Egawa S, Tsukayarna C, et al. Artificial neural network analysis for predicting pathological stage of clini-cally localized prostate cancer in the Japanese population. Jpn J Chin Oncol 2002;32:530–5.
  • Naguib RN, Robinson MC, Neal DE, et al. Neural network analysis of combined conventional and experimental prog-nostic markers in prostate cancer: a pilot study. Br J Cancer 1998;78:246–50.
  • Porter C, O'Donnell C, Crawford ED, et al. Artificial neural network model to predict biochemical failure after radical prostatectomy. Mol Urol 2001;5:159–62.
  • Remzi M, Anagnostou T, Ravery V, et al. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology 2003;62:456–60.
  • Snow PB, Smith DS, Catalona WJ. Artificial neural net-works in the diagnosis and prognosis of prostate cancer: a pilot study. J Urol 1994;152:1923–6.
  • Stephan C, Cammann H, Semjonow A, et al. Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies. Chin Chem 2002;48:1279–87.
  • Stephan C, Jung K, Cammann H, et al. An artificial neural network considerably improves the diagnostic power of percent free prostate-specific antigen in prostate cancer diagnosis: results of a 5-year investigation. Int J Cancer 2002;99:466–73.
  • Stephan C, Vogel B, Cammann H, et al. An artificial neural network as a tool in risk evaluation of prostate cancer. Indication for biopsy with the PSA range of 2–20 microg/l. Urologe A 2003;42:1221-9 (in German).
  • Tewari A, Narayan P. Novel staging tool for localized prostate cancer: a pilot study using genetic adaptive neural networks. J Urol 1998;160:430–6.
  • Tewari A, Issa M, El-Galley R, et al. Genetic adaptive neural network to predict biochemical failure after radical prosta-tectomy: a multi-institutional study. Mol Urol 2001;5:163–9.
  • Veltri RW, Chaudhari M, Miller MC, et al. Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage. Chin Chem 2002;48:1828–34.
  • Zlotta AR, Remzi M, Snow PB, et al. An artificial neural network for prostate cancer staging when serum prostate specific antigen is 10 ng./ml. or less. J Urol 2003;169:1724–8.
  • 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;20:323–42.
  • Tricoli JV, Schoenfeldt M, Conley BA. Detection of prostate cancer and predicting progression: current and future diagnostic markers. Chin Cancer Res 2004;10:3943–53.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.