1Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, Udine.
2Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli Federico II, Naples.
Neurosurg Focus. 2020 Oct;49(4):E13. doi: 10.3171/2020.7.FOCUS20420.
Approximately half of glioblastoma (GBM) cases develop in geriatric patients, and this trend is destined to increase with the aging of the population. The optimal strategy for management of GBM in elderly patients remains controversial. The aim of this study was to assess the role of surgery in the elderly (≥ 65 years old) based on clinical, molecular, and imaging data routinely available in neurosurgical departments and to assess a prognostic survival score that could be helpful in stratifying the prognosis for elderly GBM patients.
Clinical, radiological, surgical, and molecular data were retrospectively analyzed in 322 patients with GBM from 9 neurosurgical centers. Univariate and multivariate analyses were performed to identify predictors of survival. A random forest approach (classification and regression tree [CART] analysis) was utilized to create the prognostic survival score.
Survival analysis showed that overall survival (OS) was influenced by age as a continuous variable (p = 0.018), MGMT (p = 0.012), extent of resection (EOR; p = 0.002), and preoperative tumor growth pattern (evaluated with the preoperative T1/T2 MRI index; p = 0.002). CART analysis was used to create the prognostic survival score, forming six different survival groups on the basis of tumor volumetric, surgical, and molecular features. Terminal nodes with similar hazard ratios were grouped together to form a final diagram composed of five classes with different OSs (p < 0.0001). EOR was the most robust influencing factor in the algorithm hierarchy, while age appeared at the third node of the CART algorithm. The ability of the prognostic survival score to predict death was determined by a Harrell's c-index of 0.75 (95% CI 0.76-0.81).
The CART algorithm provided a promising, thorough, and new clinical prognostic survival score for elderly surgical patients with GBM. The prognostic survival score can be useful to stratify survival risk in elderly GBM patients with different surgical, radiological, and molecular profiles, thus assisting physicians in daily clinical management. The preliminary model, however, requires validation with future prospective investigations. Practical recommendations for clinicians/surgeons would strengthen the quality of the study; e.g., surgery can be considered as a first therapeutic option in the workflow of elderly patients with GBM, especially when the preoperative estimated EOR is greater than 80%.
约有一半的胶质母细胞瘤(GBM)发生在老年患者中,随着人口老龄化,这一趋势注定会增加。老年患者 GBM 的最佳治疗策略仍存在争议。本研究旨在根据神经外科常规提供的临床、分子和影像学数据,评估老年人(≥65 岁)手术的作用,并评估有助于分层老年 GBM 患者预后的生存预后评分。
回顾性分析了来自 9 个神经外科中心的 322 名 GBM 患者的临床、放射学、手术和分子数据。进行单变量和多变量分析以确定生存预测因子。采用随机森林方法(分类和回归树[CART]分析)创建预后生存评分。
生存分析表明,总生存期(OS)受年龄的影响(p=0.018)、MGMT(p=0.012)、切除范围(EOR;p=0.002)和术前肿瘤生长模式(用术前 T1/T2 MRI 指数评估;p=0.002)。CART 分析用于创建预后生存评分,根据肿瘤体积、手术和分子特征将其分为六个不同的生存组。具有相似风险比的终节点被组合在一起,形成由五个具有不同 OS 的类组成的最终图表(p<0.0001)。EOR 是算法层次结构中最具影响力的因素,而年龄出现在 CART 算法的第三个节点。预后生存评分预测死亡的能力由 Harrell's c 指数为 0.75(95%CI 0.76-0.81)确定。
CART 算法为接受手术治疗的老年 GBM 患者提供了一种有前途、全面和新颖的临床预后生存评分。该预后生存评分可用于分层具有不同手术、放射学和分子特征的老年 GBM 患者的生存风险,从而有助于医生进行日常临床管理。然而,初步模型需要未来的前瞻性研究来验证。为临床医生/外科医生提供实用建议将增强研究质量;例如,当术前估计的 EOR 大于 80%时,手术可被视为老年 GBM 患者治疗流程中的首选治疗方案。