Department of Surgery, Urology Service, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Future Oncol. 2009 Dec;5(10):1555-84. doi: 10.2217/fon.09.121.
Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.
前列腺癌是一种非常复杂的疾病，临床决策需要平衡临床获益、预期寿命、合并症和潜在的治疗相关副作用。准确预测临床结局有助于指导前列腺癌相关决策这一艰难过程。在这篇综述中，我们讨论了预测工具的属性，并对前列腺癌相关的预测工具进行了系统综述。工具类型包括概率公式、查找表和倾向评分表、风险分层预测工具、分类和回归树分析、列线图和人工神经网络。评估工具的标准包括区分度、校准度、可推广性、复杂程度、决策分析和处理竞争风险和条件概率的能力。本文描述了现有的预测工具及其特征，重点介绍了列线图。虽然一些工具校准良好，但很少有工具经过外部验证或与其他工具直接比较。此外，应用预测工具的临床后果需要进行全面评估。尽管如此，预测工具可以通过向患者展示针对各种治疗方案的个体化预后预测来帮助临床决策。此外，准确的工具可能会改善临床试验设计。
