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>Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study
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Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study
OBJECTIVE 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. METHODS 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. RESULTS 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). CONCLUSIONS 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%.
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机译：目标大约一半的胶质母细胞瘤（GBM）病例在老年患者中发展，这种趋势注定随着人口老龄化而增加。老年患者GBM管理的最佳策略仍存在争议。本研究的目的是评估基于神经外科部门的临床，分子和成像数据的老年人（≥65岁）在老年人（≥65岁）中的作用，并评估预后存活率，这可能有助于分层预后对于老年GBM患者。方法从9个神经外科中心的322名GBM患者中回顾性分析临床，放射性，手术和分子数据。进行单变量和多变量分析以识别存活的预测因子。随机森林方法（分类和回归树[购物车]分析）用于产生预后存活率。结果存活分析表明，整体存活率（OS）受年龄的影响为连续变量（P = 0.018），MgMT（P = 0.012），切除程度（EOR; P = 0.002）和术前肿瘤生长模式（评估术前T1 / T2 MRI指数; P = 0.002）。推车分析用于产生预后存活评分，基于肿瘤体积，手术和分子特征形成六种不同的存活组。具有类似危险比的终端节点被分组，形成由具有不同OSS的五个类组成的最终图（P <0.0001）。 EOR是算法层次中最强大的影响因素，而年龄出现在购物车算法的第三节点。预后存活评分预测死亡的能力由哈尔氏菌的C折射率为0.75（95％CI 0.76-0.81）。结论Cart算法为GBM的老年手术患者提供了有希望的彻底和新的临床预后存活率。预后存活评分可用于分析老年GBM患者的生存风险不同外科，放射性和分子型材，从而帮助医生在日常临床管理中。然而，初步模式要求验证未来的预期调查。临床医生/外科医生的实用建议将加强研究质量;例如，手术可以被认为是GBM老年患者的工作流程中的第一个治疗选择，特别是当术前估计EOR大于80％时。
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