Prediction of progression: nomograms of clinical utility

Abstract

It is difficult to determine the pathologic stage of a clinically localized prostate cancer by physical examination or imaging studies. Consequently, clinicians rely on predictive models that estimate the probability of lymph node metastases and other pathologic features from clinical factors such as the clinical T stage, the grade in the biopsy specimen, and the serum prostate-specific antigen level. These models do not, however, directly predict prognosis. In developing a tool for predicting the probability that prostate cancer might recur after treatment, we took a novel approach that focused on the risk for the individual patient. In particular, we chose to develop a tool that calculates a continuous probability of recurrence rather than placing the patient in a risk group. This represents a fundamental departure from the classical goal of staging; a departure we argue is long overdue. Clinically localized prostate cancer patients deserve the most accurate and tailored predictions available, which current staging systems do not provide. Such an individualized approach should add value in medical decision making whenever an accurate prediction of the outcome may guide treatment selection.