Population-based validation of the RANO categories for extent of resection in glioblastoma

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ID: 319292
2026
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Abstract
Abstract Background The RANO Resect group published the updated RANO categories for extent of resection in glioblastoma in 2023. Our aim was to provide a population-based validation of these categories. Methods This population based retrospective cohort study included 470 consecutive patients treated at two centers in Norway and Sweden. None of the hospitals had a strategy to aim for supramarginal resections. Postoperative contrast-enhanced T1 and FLAIR volumes were used to group the patients according to the RANO categories. Cox models and Kaplan-Meier plots were used for survival analyses. We performed subgroup analyses of patients where the Stupp protocol was initiated, MGMT methylated tumors, and elderly patients (≥ 70 years). Results Median overall survival was 16, 15, 11, and 6 months for the respective categories of supramaximal (Class 1), maximal (Class 2), submaximal resection (Class 3) and biopsy only (Class 4). Thirty-five patients (7.5%) were classified as Class 1, despite not aiming for resection beyond the contrast-enhancing tumor. This class had a median tumor volume of only 6 mL, smaller preoperative FLAIR volumes and was associated with improved survival compared to Class 2 (HR = 0.6, P < .0001) in the adjusted analysis. There was no significant difference between classes 3 and 4. Discussion and Conclusion The RANO categories were associated with survival, but survival was shorter and differences across categories were less than in the original study. Maximal and supramaximal resections were associated with longer survival. One fourth of complete resections were incidentally classified as supramaximal resections, and these patients had a favorable prognosis.
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openalex_W7166858693 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Claes Johnstad, Ingerid Reinertsen, David Bouget, Okizeva Rapi, Asgeir S Jakola, Ole Solheim
Journal Neuro-Oncology Advances
Year 2026
DOI
10.1093/noajnl/vdag170
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