Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Clin Cancer Res. 2024 Nov 1;30(21):4866-4875. doi: 10.1158/1078-0432.CCR-23-3845.
To propose a novel recursive partitioning analysis (RPA) classification model in patients with IDH-wildtype glioblastomas that incorporates the recently expanded conception of the extent of resection (EOR) in terms of both supramaximal and total resections.
This multicenter cohort study included a developmental cohort of 622 patients with IDH-wildtype glioblastomas from a single institution (Severance Hospital) and validation cohorts of 536 patients from three institutions (Seoul National University Hospital, Asan Medical Center, and Heidelberg University Hospital). All patients completed standard treatment including concurrent chemoradiotherapy and underwent testing to determine their IDH mutation and MGMTp methylation status. EORs were categorized into either supramaximal, total, or non-total resections. A novel RPA model was then developed and compared with a previous Radiation Therapy Oncology Group (RTOG) RPA model.
In the developmental cohort, the RPA model included age, MGMTp methylation status, Karnofsky performance status, and EOR. Younger patients with MGMTp methylation and supramaximal resections showed a more favorable prognosis [class I: median overall survival (OS) 57.3 months], whereas low-performing patients with non-total resections and without MGMTp methylation showed the worst prognosis (class IV: median OS 14.3 months). The prognostic significance of the RPA was subsequently confirmed in the validation cohorts, which revealed a greater separation between prognostic classes for all cohorts compared with the previous RTOG RPA model.
The proposed RPA model highlights the impact of supramaximal versus total resections and incorporates clinical and molecular factors into survival stratification. The RPA model may improve the accuracy of assessing prognostic groups. See related commentary by Karschnia et al., p. 4811.
提出一种新的递归分区分析(RPA)分类模型,该模型纳入了最近提出的最大限度切除(EOR)概念,包括最大限度和完全切除。
本多中心队列研究纳入了单一机构(首尔峨山医院)的 622 例 IDH 野生型胶质母细胞瘤患者的发展队列和来自三个机构(首尔国立大学医院、 延世大学附属医院和海德堡大学医院)的 536 例患者的验证队列。所有患者均接受标准治疗,包括同期放化疗,并接受检测以确定其 IDH 突变和 MGMTp 甲基化状态。EOR 分为最大限度切除、完全切除或非完全切除。然后开发了一个新的 RPA 模型,并与之前的放射治疗肿瘤学组(RTOG)RPA 模型进行比较。
在发展队列中,RPA 模型包括年龄、MGMTp 甲基化状态、卡氏功能状态和 EOR。MGMTp 甲基化且最大限度切除的年轻患者预后较好[I 级:中位总生存期(OS)57.3 个月],而表现不佳、非完全切除且无 MGMTp 甲基化的患者预后最差[IV 级:中位 OS 14.3 个月]。随后在验证队列中证实了 RPA 的预后意义,与之前的 RTOG RPA 模型相比,所有队列的预后分类之间的分离度更大。
所提出的 RPA 模型强调了最大限度切除与完全切除的影响,并将临床和分子因素纳入生存分层。RPA 模型可能提高评估预后组的准确性。另见 Karschnia 等人的相关评论,第 4811 页。