Kattan Michael W, Scardino Peter T
Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Clin Prostate Cancer. 2002 Sep;1(2):90-6. doi: 10.3816/cgc.2002.n.010.
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.
通过体格检查或影像学研究很难确定临床局限性前列腺癌的病理分期。因此，临床医生依赖预测模型，这些模型根据临床因素（如临床T分期、活检标本中的分级以及血清前列腺特异性抗原水平）来估计淋巴结转移和其他病理特征的概率。然而，这些模型并不能直接预测预后。在开发一种预测前列腺癌治疗后复发概率的工具时，我们采用了一种新颖的方法，该方法侧重于个体患者的风险。特别是，我们选择开发一种计算复发连续概率的工具，而不是将患者归入风险组。这与分期的经典目标有根本的不同；我们认为这种不同早就应该出现了。临床局限性前列腺癌患者理应得到现有最准确、最个性化的预测，而目前的分期系统无法提供。每当准确的结果预测可能指导治疗选择时，这种个体化方法都应在医疗决策中增加价值。
