新诊断胶质母细胞瘤术后结果临床风险模型的开发与验证:RANO resect 小组的报告。

IF 16.4 1区 医学 Q1 CLINICAL NEUROLOGY
Philipp Karschnia, Jacob S Young, Gilbert C Youssef, Antonio Dono, Levin Häni, Tommaso Sciortino, Francesco Bruno, Stephanie T Juenger, Nico Teske, Jorg Dietrich, Michael Weller, Michael A Vogelbaum, Martin van den Bent, Juergen Beck, Niklas Thon, Jasper K W Gerritsen, Shawn Hervey-Jumper, Daniel P Cahill, Susan M Chang, Roberta Rudà, Lorenzo Bello, Oliver Schnell, Yoshua Esquenazi, Maximilian I Ruge, Stefan J Grau, Raymond Y Huang, Patrick Y Wen, Mitchel S Berger, Annette M Molinaro, Joerg-Christian Tonn
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引用次数: 0

摘要

背景:手术后,新确诊的胶质母细胞瘤患者经常会参加临床试验。有必要进行细致的风险评估,以减少研究臂之间的不平衡。在此,我们旨在(I)分析残留肿瘤与临床和分子因素对预后的交互影响;(II)定义术后风险评估工具:RANO resect小组回顾性地汇编了一个由七个中心组成的新诊断胶质母细胞瘤患者国际培训队列。分析了残留肿瘤与分子或临床因素和生存期的综合关联,并进行了递归分区分析以建立风险模型。所建立的模型在另一个外部验证队列中进行了预后验证:我们的训练队列包括1003名新确诊的异柠檬酸脱氢酶野生型胶质母细胞瘤患者。残留肿瘤、O6-甲基鸟嘌呤DNA甲基转移酶(MGMT)启动子甲基化状态、年龄和术后KPS都是预后生存的因素,并被纳入回归树分析。通过对预后因素进行单独加权,综合这四个变量的加权得分(范围为 0-9 分)可将生存率较低的患者区分为低危(0-2 分)、中危(3-5 分)和高危(6-9 分)。我们的风险模型在基于治疗的亚组中保留了预后价值,并在由 258 名胶质母细胞瘤患者组成的外部验证队列中得到了证实。与之前推测的模型相比,我们的模型的拟合优度更高:结论:新型 RANO 风险模型是一种易于使用但预后性很高的工具,可用于术后患者进一步治疗前的分层。该模型可用于指导患者管理,减少前瞻性试验中研究臂之间的不平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a clinical risk model for postoperative outcome in newly diagnosed glioblastoma: a report of the RANO resect group.

Background: Following surgery, patients with newly diagnosed glioblastoma frequently enter clinical trials. Nuanced risk assessment is warranted to reduce imbalances between study arms. Here, we aimed (I) to analyze the interactive effects of residual tumor with clinical and molecular factors on outcome and (II) to define a postoperative risk assessment tool.

Methods: The RANO resect group retrospectively compiled an international, seven-center training cohort of patients with newly diagnosed glioblastoma. The combined associations of residual tumor with molecular or clinical factors and survival were analyzed, and recursive partitioning analysis was performed for risk modeling. The resulting model was prognostically verified in a separate external validation cohort.

Results: Our training cohort compromised 1003 patients with newly diagnosed isocitrate dehydrogenase-wildtype glioblastoma. Residual tumor, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, age, and postoperative KPS were prognostic for survival and incorporated into regression tree analysis. By individually weighting the prognostic factors, an additive score (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for inferior survival. The prognostic value of our risk model was retained in treatment-based subgroups and confirmed in an external validation cohort of 258 patients with glioblastoma. Compared to previously postulated models, goodness-of-fit measurements were superior for our model.

Conclusions: The novel RANO risk model serves as an easy-to-use, yet highly prognostic tool for postoperative patient stratification prior to further therapy. The model may serve to guide patient management and reduce imbalances between study arms in prospective trials.

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来源期刊
Neuro-oncology
Neuro-oncology 医学-临床神经学
CiteScore
27.20
自引率
6.30%
发文量
1434
审稿时长
3-8 weeks
期刊介绍: Neuro-Oncology, the official journal of the Society for Neuro-Oncology, has been published monthly since January 2010. Affiliated with the Japan Society for Neuro-Oncology and the European Association of Neuro-Oncology, it is a global leader in the field. The journal is committed to swiftly disseminating high-quality information across all areas of neuro-oncology. It features peer-reviewed articles, reviews, symposia on various topics, abstracts from annual meetings, and updates from neuro-oncology societies worldwide.
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