Nicholas F. Marko, Frederick F. Lang, Dima Suki, and Raymond E. Sawaya, The University of Texas MD Anderson Cancer Center, Houston, TX; Robert J. Weil and Jason L. Schroeder, Cleveland Clinic, Cleveland, OH.
J Clin Oncol. 2014 Mar 10;32(8):774-82. doi: 10.1200/JCO.2013.51.8886. Epub 2014 Feb 10.
Approximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy.
We used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution.
AFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival.
Nonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR thresholds and instead provides the first explicit evidence supporting a maximum safe resection approach to glioblastoma surgery.
美国每年约诊断出 12000 例胶质母细胞瘤。 这种疾病的中位生存率为 12 个月，但个体生存率可能因患者特定因素而异，包括手术切除范围（EOR）。 我们研究的目的是开发一种可靠的策略，用于个性化生存预测，并量化生存、EOR 和辅助放化疗之间的关系。
我们使用来自 721 例新诊断的胶质母细胞瘤患者（1993 年至 2010 年）的数据，使用加速失效时间（AFT）建模来模拟影响手术后个体化生存的因素，并用该模型构建用于生存预测的概率性、个体化工具。我们使用来自第二个机构的 109 例患者的独立数据验证了该模型。
使用年龄、卡诺夫斯基表现评分、EOR 和辅助放化疗的 AFT 建模为胶质母细胞瘤生成了一个连续的、非线性的多变量生存模型。 个性化预测误差的中位数为 4.37 个月，比当前方法提高了 20%以上。 随后基于模型的计算得出了 EOR 和辅助治疗对生存影响的患者特异性预测。
非线性、多变量 AFT 建模优于当前估计胶质母细胞瘤切除后个体生存的方法。 该模型生成个性化生存曲线，并量化了调节患者特异性生存的变量之间的关系。 这种方法提供了关于疾病预期病程、总体预后以及 EOR 和辅助放化疗对患者特异性影响的全面、个性化、概率性和临床相关信息。 预期中位生存与 EOR 之间确定的连续、非线性关系反对基于刚性 EOR 阈值的手术管理策略，并首次明确支持最大安全切除方法作为胶质母细胞瘤手术的策略。
