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Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential
Neurosurgical Review ( IF 2.8 ) Pub Date : 2020-11-06 , DOI: 10.1007/s10143-020-01430-z
Ishaan Ashwini Tewarie 1, 2, 3 , Joeky T Senders 1, 3 , Stijn Kremer 1 , Sharmila Devi 3, 4 , William B Gormley 3 , Omar Arnaout 3 , Timothy R Smith 3 , Marike L D Broekman 1, 3, 5
Affiliation  

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58–0.98), accuracy (0.69–0.98), and C-index (0.66–0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.



中文翻译:

胶质母细胞瘤患者的生存预测——我们到了吗?胶质母细胞瘤预后模型及其临床潜力的系统评价

胶质母细胞瘤与预后不良有关。尽管在人群水平上对生存统计数据进行了很好的描述,但尽管预后模型的数量不断增加,但预测个体患者的预后仍然具有挑战性。本研究的目的是系统地回顾有关胶质母细胞瘤患者预后建模的文献。进行了系统的文献搜索,以确定所有相关研究,这些研究开发了一个预后模型,用于按照 PRISMA 指南预测胶质母细胞瘤患者的总生存期。根据预后模型审查参与者、输入类型、算法类型、验证和测试程序。在 595 次引文中,27 项研究被纳入定性审查。纳入的研究共开发和评估了 59 个模型,其中只有七个在不同的患者队列中得到了外部验证。根据 AUC (0.58–0.98)、准确性 (0.69–0.98) 和 C 指数 (0.66–0.70),这些研究的预测性能差异很大。三项研究将他们的模型部署为在线预测工具,所有这些都基于统计算法。生存预测模型不断提高的性能将有助于胶质母细胞瘤患者的个性化临床决策。科学领域倾向于使用基于高维数据开发的机器学习模型,通常会产生有希望的结果。然而,这些模型都没有应用于临床护理。为促进高性能生存预测模型的临床实施,未来的工作应侧重于协调数据采集方法,

更新日期:2020-11-06
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