Prognostic revalidation of RANO categories for extent of resection in glioblastoma: a reconstruction of individual patient data.

IF 3.2 2区医学 Q2 CLINICAL NEUROLOGY

Journal of Neuro-Oncology Pub Date : 2025-05-01 Epub Date: 2025-02-24 DOI:10.1007/s11060-025-04950-0

Johannes Wach, Martin Vychopen, Erdem Güresir

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引用次数: 0

Abstract

Background: The RANO classification for glioblastoma defines resection categories based on volumetric tumor assessments, aiming to standardize outcomes related to extent of resection (EOR). This study revalidates the prognostic impact of RANO classes by reconstructing individual patient data (IPD).

Methods: A systematic review and meta-analysis were performed, including three studies comprising 580 glioblastoma patients. Included studies reported or allowed conversion to RANO classes for glioblastoma resection extent, with detailed OS data and numbers at risk. Overall survival (OS) data were extracted from Kaplan-Meier survival curves, and IPD were reconstructed using Digitizelt and the R package IPDfromKM. Survival analyses were conducted using Kaplan-Meier estimates and Cox regression models.

Results: Median follow-up was 15.6 months (IQR: 10.1-28.8). Patients undergoing supramaximal resection (RANO class 1, n = 163) had the highest median OS (35.6 months; 95% CI: 30.9-40.4), significantly outperforming non-class 1 resections (median OS: 13.9 months; 95% CI: 13.0-14.7; p < 0.001). Subgroup analysis revealed superior OS for class 2a (19.0 months) over class 2b (14.1 months; p < 0.001), while class 3 and 4 resections demonstrated progressively poorer outcomes. Hazard ratios consistently favored class 1 versus all other classes (HR: 0.28; 95% CI: 0.23-0.37).

Conclusions: Supramaximal (class 1) resection provides a significant survival benefit in glioblastoma, underscoring its critical role in surgical management. The RANO classification stratifies resection outcomes effectively, supporting its use as a prognostic tool. These findings advocate for resection strategies targeting maximal tumor removal.

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胶质母细胞瘤切除术范围的RANO分类的预后再验证：个体患者数据的重建。

背景：胶质母细胞瘤的RANO分类根据肿瘤体积评估来定义切除类别，旨在标准化与切除程度（EOR）相关的结果。本研究通过重建个体患者数据（IPD）来重新验证RANO分类对预后的影响。方法：系统回顾和荟萃分析，包括3项研究，包括580例胶质母细胞瘤患者。纳入的研究报告或允许将胶质母细胞瘤的切除程度转换为RANO分类，并提供详细的OS数据和风险人数。从Kaplan-Meier生存曲线中提取总生存期（OS）数据，并使用digittizelt和R包IPDfromKM重建IPD。生存率分析采用Kaplan-Meier估计和Cox回归模型。结果：中位随访时间为15.6个月（IQR: 10.1 ~ 28.8）。接受上大骨切除术的患者（RANO 1级，n = 163）的中位生存期最高(35.6个月；95% CI: 30.9-40.4)，显著优于非1类切除(中位OS: 13.9个月；95% ci: 13.0-14.7；结论：上极（1级）切除可显著提高胶质母细胞瘤的生存率，强调其在手术治疗中的关键作用。RANO分类有效地对切除结果进行分层，支持其作为预后工具的使用。这些发现提倡以最大限度切除肿瘤为目标的切除策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Neuro-Oncology 医学-临床神经学

CiteScore

6.60

自引率

7.70%

发文量

277

审稿时长

3.3 months

期刊介绍： The Journal of Neuro-Oncology is a multi-disciplinary journal encompassing basic, applied, and clinical investigations in all research areas as they relate to cancer and the central nervous system. It provides a single forum for communication among neurologists, neurosurgeons, radiotherapists, medical oncologists, neuropathologists, neurodiagnosticians, and laboratory-based oncologists conducting relevant research. The Journal of Neuro-Oncology does not seek to isolate the field, but rather to focus the efforts of many disciplines in one publication through a format which pulls together these diverse interests. More than any other field of oncology, cancer of the central nervous system requires multi-disciplinary approaches. To alleviate having to scan dozens of journals of cell biology, pathology, laboratory and clinical endeavours, JNO is a periodical in which current, high-quality, relevant research in all aspects of neuro-oncology may be found.