Karschnia Philipp, Young Jacob S, Youssef Gilbert C, Dono Antonio, Häni Levin, Sciortino Tommaso, Bruno Francesco, Juenger Stephanie T, Teske Nico, Dietrich Jorg, Weller Michael, Vogelbaum Michael A, van den Bent Martin, Beck Juergen, Thon Niklas, Gerritsen Jasper K W, Hervey-Jumper Shawn, Cahill Daniel P, Chang Susan M, Rudà Roberta, Bello Lorenzo, Schnell Oliver, Esquenazi Yoshua, Ruge Maximilian I, Grau Stefan J, Huang Raymond Y, Wen Patrick Y, Berger Mitchel S, Molinaro Annette M, Tonn Joerg-Christian
Department of Neurosurgery, FAU University Hospital of the Friedrich-Alexander-University, Erlangen, Germany.
Department of Neurosurgery, LMU University Hospital of the Ludwig-Maximilians-University Munich, Munich, Germany.
Neuro Oncol. 2025 May 15;27(4):1046-1060. doi: 10.1093/neuonc/noae231.
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.
The response assessment in neuro-oncology (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.
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 Karnofsky Performance Score 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.
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.
手术后，新诊断的胶质母细胞瘤患者经常进入临床试验。需要进行细致的风险评估以减少研究组之间的不平衡。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作用，以及（II）定义一种术后风险评估工具。
神经肿瘤学反应评估（RANO）切除组回顾性汇编了一个国际多中心的新诊断胶质母细胞瘤患者训练队列。分析了残留肿瘤与分子或临床因素及生存的联合关联，并进行递归划分分析以建立风险模型。在一个单独的外部验证队列中对所得模型进行预后验证。
我们的训练队列包括1003例新诊断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、年龄和术后卡诺夫斯基表现评分对生存具有预后意义，并纳入回归树分析。通过对预后因素进行单独加权，整合这四个变量的相加评分（范围为0 - 9分）区分出低风险（0 - 2分）、中度风险（3 - 5分）和高风险（6 - 9分）的生存较差患者。我们风险模型的预后价值在基于治疗的亚组中得以保留，并在258例胶质母细胞瘤患者的外部验证队列中得到证实。与先前假设的模型相比，我们模型的拟合优度测量结果更优。
新的RANO风险模型是一种易于使用但预后性很强的工具，可用于进一步治疗前的术后患者分层。该模型可用于指导患者管理，并减少前瞻性试验中研究组之间的不平衡。
