Crawford E David
Section of Urologic Oncology, Division of Urology, University of Colorado Health Science Center and the University of Colorado Cancer Center, Denver, Colorado 80262, USA.
Urology. 2003 Dec 22;62(6 Suppl 1):13-9. doi: 10.1016/j.urology.2003.10.008.
The National Comprehensive Cancer Network (NCCN) developed a series of algorithms based on expert opinion to guide the treatment of patients with prostate cancer. These algorithms define acceptable treatment options according to the risk of disease recurrence and the life expectancy of the patient. However, practicing clinicians are expected to use medical judgment when making actual treatment decisions. Many clinical and pathologic variables affect patient prognosis, which, in turn, influences the treatment and surveillance of patients. Artificial neural networks (ANNs) offer promise for improving the predictive value of traditional statistical modeling. ANN models have been designed that predict risk of lymph node spread and capsular involvement during disease staging, risk of disease recurrence after prostatectomy, and overall and cause-specific survival. This article provides a review of guidelines, such as NCCN and ANN, used for the management of prostate cancer and suggests that group-level recommendations based on these algorithms or other decision trees may misrepresent individual patient preferences for treatment. Patients and their clinicians need to consider available prognostic information, including clinical status, pathologic variables, and comorbidities, and then select a reasonable treatment approach that maximizes outcome and quality of life according to the preferences of each patient.
