Department of General Gynecology and Gynecologic Oncology, Comprehensive Cancer Center, Medical University of Vienna, Austria.
Br J Cancer. 2012 Sep 4;107(6):918-24. doi: 10.1038/bjc.2012.340. Epub 2012 Aug 7.
Nomograms are predictive tools that are widely used for estimating cancer prognosis. The aim of this study was to develop a nomogram for the prediction of overall survival (OS) in patients diagnosed with cervical cancer.
Cervical cancer databases of two large institutions were analysed. Overall survival was defined as the clinical endpoint and OS probabilities were estimated using the Kaplan-Meier method. Based on the results of survival analyses and previous studies, relevant covariates were identified, a nomogram was constructed and validated using bootstrap cross-validation. Discrimination of the nomogram was quantified with the concordance probability.
In total, 528 consecutive patients with invasive cervical cancer, who had all nomogram variables available, were identified. Mean 5-year OS rates for patients with International Federation of Gynecologists and Obstetricians (FIGO) stage IA, IB, II, III, and IV were 99.0%, 88.6%, 65.8%, 58.7%, and 41.5%, respectively. Seventy-six cancer-related deaths were observed during the follow-up period. FIGO stage, tumour size, age, histologic subtype, lymph node ratio, and parametrial involvement were selected as nomogram covariates. The prognostic performance of the model exceeded that of FIGO stage alone and the model's estimated optimism-corrected concordance probability was 0.723, indicating accurate prediction of OS. We present the prediction model as nomogram and provide a web-based risk calculator (http://www.ccc.ac.at/gcu).
Based on six easily available parameters, a novel statistical model to predict OS of patients diagnosed with cervical cancer was constructed and validated. The model was implemented in a nomogram and provides accurate prediction of individual patients' prognosis useful for patient counselling and deciding on follow-up strategies.
列线图是一种广泛用于估计癌症预后的预测工具。本研究旨在为宫颈癌患者的总生存（OS）预测建立一个列线图。
分析了两个大型机构的宫颈癌数据库。总生存被定义为临床终点，OS 概率使用 Kaplan-Meier 方法估计。基于生存分析和既往研究的结果，确定了相关协变量，构建并使用 Bootstrap 交叉验证验证了列线图。通过一致性概率量化了列线图的判别能力。
共纳入了 528 例连续的浸润性宫颈癌患者，所有患者均具有列线图变量。FIGO 分期 IA、IB、II、III 和 IV 的患者 5 年 OS 率分别为 99.0%、88.6%、65.8%、58.7%和 41.5%。随访期间观察到 76 例癌症相关死亡。FIGO 分期、肿瘤大小、年龄、组织学亚型、淋巴结比值和宫旁累及被选为列线图协变量。模型的预后性能优于 FIGO 分期，模型估计的校正一致性概率为 0.723，表明 OS 预测准确。我们以列线图的形式呈现预测模型，并提供了一个基于网络的风险计算器（http://www.ccc.ac.at/gcu）。
基于 6 个易于获得的参数，构建并验证了一种用于预测宫颈癌患者 OS 的新统计模型。该模型已在列线图中实现，可对个体患者的预后进行准确预测，有助于患者咨询和决定随访策略。
