Atlas of the Prostate pp 115-122 | Cite as
Decision Making for Clinically Localized Prostate Cancer
Abstract
Treatment decision making for patients with clinically localized prostate cancer can be difficult. Because of the age of men at diagnosis, survival benefits associated with aggressive therapies such as surgery typically are estimated to be small, and patient preferences are very influential in determining the preferred treatment. This chapter reviews two different methods for assisting patients in choosing the treatment that is best for them. First, we consider the decision analytic approach, which directly incorporates patient preferences and survival estimates in suggesting the appropriate treatment. Because this approach is difficult to perform at the bedside, we also discuss a second method, nomograms, which are mathematical models that predict outcomes for the individual patient.
Keywords
Radical Prostatectomy Localize Prostate Cancer Watchful Waiting Perfect Health Seminal Vesicle InvasionPreview
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References
- 1.Kattan MW, Cowen ME, Miles BJ: A decision analysis for treatment of clinically localized prostate cancer. J Gen Intern Med 1997, 12: 299–305.PubMedCrossRefGoogle Scholar
- 2.Kattan MW, Fearn PA, Miles BJ: Time trade-off utility modified to accommodate degenerative and life-threatening conditions. In Proceedings of the 2001 AMIA Annual Symposium. Bethesda: American Medical Informatics Association; 2001: 304–308.Google Scholar
- 3.Kattan MW, Adams DA, Parko MS: A comparison of machine learning with human judgement. J Manage lnf Surg 1993, 9: 37–57.Google Scholar
- 4.Cowen ME, Miles BJ, Cahill DE, et al.: The danger of applying group-level decision analyses for the treatment of localized prostate cancer to the individual. Medical Decision Making 1998, 18:376–380.PubMedCrossRefGoogle Scholar
- 5.Ross PL, Gerigk C, Gonen M, et al.: Comparisons of nomograms and urologists predictions in prostate cancer. Sem Urol Oncol 2002, 20:82–88.CrossRefGoogle Scholar
- 6.Partin AW, Yoo J, Carter HB, et al.: The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer.J Urol 1993, 150:110–114.PubMedGoogle Scholar
- 7.Partin AW, Kattan MW, Subong ENP, et al.: Combination of PSA, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer: a multi-institutional update. JAMA 1997, 277:1445–1451.PubMedCrossRefGoogle Scholar
- 8.Kattan MW, Stapleton AMF, Wheeler TM, Scardino PT: Evaluation of a nomogram for predicting pathological stage of men with clinically localized prostate cancer. Cancer 1997, 79: 528–537.PubMedCrossRefGoogle Scholar
- 9.Kattan MW, Eastham JA, Stapleton AMF, et al.: A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 1998, 90:766–771.PubMedCrossRefGoogle Scholar
- 10.Kattan MW, Potters L, Blasko JC, et al.: Pretreatment nomogram for predicting freedom from recurrence following permanent prostate brachytherapy in prostate cancer. Urology, 2001 58:393–399.PubMedCrossRefGoogle Scholar
- 11.Kattan MW, Zelefsky MJ, Scardino PT, et al.: Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer. J Clin Oncol2000, 18:3352–3359.Google Scholar
- 12.Kattan MW: Statistical prediction models, artificial neural networks, and the sophism “I am a patient, not a statistic.” J Clin Oncol 2002, 20: 885–887.PubMedCrossRefGoogle Scholar