Department of Radiation Oncology, London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada.
Clin Oncol (R Coll Radiol). 2013 Apr;25(4):227-35. doi: 10.1016/j.clon.2012.11.006. Epub 2012 Dec 5.
Prognostic indices are commonly used in the context of brain metastases radiotherapy to guide patient decision-making and clinical trial stratification. The purpose of this investigation was to compare nine published brain metastases prognostic indices using traditional and novel statistical comparison metrics.
A retrospective review was carried out on two institutional databases of 501 patients diagnosed with brain metastatic disease, who received either stereotactic radiosurgery (n = 381) or fractionated stereotactic radiation therapy (n = 120) between 2002 and 2011. Descriptive statistics were generated for patient, tumour and treatment factors, as well as prognostic indices distribution. To identify predictors of overall survival, Kaplan-Meier estimates and multivariable Cox proportional hazard analyses were carried out. Prognostic indices were compared with each other using novel metrics, including: net reclassification improvement (NRI) index, integrated discrimination improvement (IDI) index and decision curve analysis (DCA).
Multivariable Cox modelling confirmed the importance of all individual prognostic indices component factors except for 'active primary cancer' tumour status. When traditional and novel comparative metrics were incorporated, the available published prognostic indices were found to have important general classification benefits as follows: Radiation Therapy Oncology Group recursive partitioning analysis (RTOG RPA; NRI and DCA), Rades et al. first index (RADES I; IDI and DCA), Golden grading system (GGS; IDI and DCA) and Rotterdam index (RDAM; major misclassification rate and NRI). The graded prognostic assessment system had the smallest misclassification rate (5%) in terms of high-risk (i.e. poor prognosis) classification.
Summarising the various comparative approaches used in this report, we found that the RTOG RPA, GGS, RADES I and RDAM systems were superior in more than one metric studied. Of these, only the RTOG RPA has been extensively validated using large datasets and clinically utilised both at the patient level and in clinical trials.
预后指数常用于脑转移放射治疗,以指导患者决策和临床试验分层。本研究旨在使用传统和新的统计比较指标比较 9 种已发表的脑转移瘤预后指数。
对 2002 年至 2011 年间在两个机构数据库中诊断为脑转移疾病的 501 例患者进行回顾性分析,这些患者接受立体定向放射外科治疗(n=381)或分次立体定向放射治疗(n=120)。对患者、肿瘤和治疗因素以及预后指数分布进行描述性统计。为了确定总生存的预测因素,进行 Kaplan-Meier 估计和多变量 Cox 比例风险分析。使用新指标,包括净重新分类改善(NRI)指数、综合判别改善(IDI)指数和决策曲线分析(DCA),对预后指数进行相互比较。
多变量 Cox 模型证实了除“活跃的原发性癌症”肿瘤状态外,所有个体预后指数组成因素的重要性。当纳入传统和新的比较指标时,发现现有的已发表的预后指数具有重要的总体分类优势,如下所示:放射治疗肿瘤组递归分区分析(RTOG RPA;NRI 和 DCA)、Rades 等人的第一个指数(RADES I;IDI 和 DCA)、Golden 分级系统(GGS;IDI 和 DCA)和鹿特丹指数(RDAM;主要分类错误率和 NRI)。在高危(即预后不良)分类方面,分级预后评估系统的分类错误率(5%)最小。
根据本报告中使用的各种比较方法总结,我们发现 RTOG RPA、GGS、RADES I 和 RDAM 系统在研究的多个指标中表现优异。其中,只有 RTOG RPA 已通过大型数据集进行了广泛验证,并在患者层面和临床试验中得到广泛应用。