SynthesisJournal of neuro-oncology2025
Prognostic revalidation of RANO categories for extent of resection in glioblastoma: a reconstruction of individual patient data.
Synthesis in Journal of neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- When Surgical Innovation Outpaces Evidence: Does Modern Maximal Resection Require Re-Evaluation of Postoperative Radiotherapy in Glioblastoma?Medical sciences (Basel, Switzerland) · 2026Review
- Extent of resection and perioperative neurological status as prognostic markers in glioblastoma: a population-based study.Journal of neuro-oncology · 2026Article
- Surgical decision making in the era of supramarginal glioma resections: a current perspective and narrative review.CNS oncology · 2025Review
- GLIMMER: an interim subgroup analysis from an ongoing prospective study evaluating hyperspectral imaging for MGMT promoter methylation in gliomas.Journal of neuro-oncology · 2025Observational
- Population-based validation of the RANO categories for extent of resection in glioblastoma.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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).
methodsA 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.
resultsMedian 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).
conclusionsSupramaximal (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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