Università Cattolica S. Cuore, Rome.
J Clin Oncol. 2011 Aug 10;29(23):3163-72. doi: 10.1200/JCO.2010.33.1595. Epub 2011 Jul 11.
PURPOSE: The purpose of this study was to develop accurate models and nomograms to predict local recurrence, distant metastases, and survival for patients with locally advanced rectal cancer treated with long-course chemoradiotherapy (CRT) followed by surgery and to allow for a selection of patients who may benefit most from postoperative adjuvant chemotherapy and close follow-up. PATIENTS AND METHODS: All data (N = 2,795) from five major European clinical trials for rectal cancer were pooled and used to perform an extensive survival analysis and to develop multivariate nomograms based on Cox regression. Data from one trial was used as an external validation set. The variables used in the analysis were sex, age, clinical tumor stage stage, tumor location, radiotherapy dose, concurrent and adjuvant chemotherapy, surgery procedure, and pTNM stage. Model performance was evaluated by the concordance index (c-index). Risk group stratification was proposed for the nomograms. RESULTS: The nomograms are able to predict events with a c-index for external validation of local recurrence (LR; 0.68), distant metastases (DM; 0.73), and overall survival (OS; 0.70). Pathologic staging is essential for accurate prediction of long-term outcome. Both preoperative CRT and adjuvant chemotherapy have an added value when predicting LR, DM, and OS rates. The stratification in risk groups allows significant distinction between Kaplan-Meier curves for outcome. CONCLUSION: The easy-to-use nomograms can predict LR, DM, and OS over a 5-year period after surgery. They may be used as decision support tools in future trials by using the three defined risk groups to select patients for postoperative chemotherapy and close follow-up (http://www.predictcancer.org).
目的:本研究旨在开发准确的模型和列线图,以预测接受长程放化疗(CRT)后手术治疗的局部晚期直肠癌患者的局部复发、远处转移和生存情况,并为可能从术后辅助化疗和密切随访中获益最大的患者选择提供依据。
患者和方法:汇总了来自五个主要欧洲直肠癌临床试验的所有数据(N=2795),以进行广泛的生存分析,并基于 Cox 回归开发多变量列线图。一个试验的数据被用作外部验证集。分析中使用的变量包括性别、年龄、临床肿瘤分期、肿瘤位置、放疗剂量、同期和辅助化疗、手术方式以及 pTNM 分期。通过一致性指数(c-index)评估模型性能。为列线图提出了风险组分层。
结果:列线图能够预测外部验证集的局部复发(LR;0.68)、远处转移(DM;0.73)和总生存(OS;0.70)事件。病理分期对于准确预测长期预后至关重要。术前 CRT 和辅助化疗在预测 LR、DM 和 OS 率方面均具有附加价值。风险组分层可显著区分生存结果的 Kaplan-Meier 曲线。
结论:易于使用的列线图可预测术后 5 年内的 LR、DM 和 OS。它们可以作为未来试验中的决策支持工具,通过定义的三个风险组选择接受术后化疗和密切随访的患者(http://www.predictcancer.org)。
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