Division of Urology; Sidney Kimmel Center for Prostate and Urologic Cancer, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 27, New York, NY 10065, USA.
Ther Adv Urol. 2009 Apr;1(1):13-26. doi: 10.1177/1756287209103923.
Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with bladder cancer. In this review, we discuss the criteria for the evaluation of nomograms and review current available nomograms for advanced bladder cancer.
A retrospective review of the Pubmed database between 2002 and 2008 was performed using the keywords 'nomogram' and 'bladder'. We limited the articles to advanced bladder cancer. We recorded input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed.
We discuss the characteristics needed to evaluate nomograms such as predictive accuracy, calibration, generalizability, level of complexity, effect of competing risks, conditional probabilities, and head-to-head comparison with other prediction methods. The predictive accuracies of the pre-cystectomy tools (n = 2) range from ∼65-75% and that of the post-cystectomy tools (n = 5) range from ∼75-80%. While some of these nomograms are well-calibrated and outperform AJCC staging, none has been externally validated. To date, four studies demonstrated a statistically significant improvement in predictive accuracy of nomograms by including biomarkers.
Nomograms provide accurate individualized estimates of outcomes. They currently represent the most accurate and discriminatory decision-making aids tools for predicting outcomes in patients with bladder cancer. Use of current nomograms could improve current selection of patients for standard therapy and investigational trial design by ensuring homogeneous groups. The addition of biological markers to the currently available nomograms using clinical and pathologic data holds the promise of improving prediction and refining management of patients with bladder cancer.
如果医生要向膀胱癌患者推荐特定的治疗方案，那么准确评估风险至关重要。在本综述中，我们讨论了评价列线图的标准，并回顾了目前可用于晚期膀胱癌的列线图。
我们使用关键词“列线图”和“膀胱”对 2002 年至 2008 年期间的 Pubmed 数据库进行了回顾性分析。我们将文章限定为晚期膀胱癌。我们记录了输入变量、预测形式、用于开发预测工具的患者数量、预测的结果、预测工具的具体特征、预测准确性以及是否进行了验证。
我们讨论了评估列线图所需的特征，包括预测准确性、校准、通用性、复杂程度、竞争风险的影响、条件概率以及与其他预测方法的头对头比较。术前工具（n=2）的预测准确性范围约为 65%-75%，术后工具（n=5）的预测准确性范围约为 75%-80%。虽然其中一些列线图校准良好且优于 AJCC 分期，但均未进行外部验证。迄今为止，四项研究通过纳入生物标志物证明了列线图预测准确性的统计学显著提高。
列线图提供了准确的个体化预后估计。它们目前是预测膀胱癌患者预后最准确和最具区分度的决策辅助工具。使用当前的列线图可以通过确保同质组来改善对标准治疗的患者选择和临床试验设计。将临床和病理数据中目前可用的列线图与生物标志物相结合，有望改善预测并细化膀胱癌患者的管理。
