Retrospective study shows alignment of progression-free survival with overall survival in glioblastoma, suggesting improved risk stratification using mRANO and RANO 2.0.
INTRODUCTION In this retrospective study, we compared three response assessment criteria - RANO 2.0, mRANO, and RANO - in patients with newly diagnosed glioblastoma (GB) treated with standard-of-care therapy. We evaluated whether progression-free survival (PFS), as defined by each criterion, serves as a prognostic marker for overall survival (OS). Additionally, we performed a landmark analysis at 8 months to evaluate the impact of progression status on patient outcomes. METHODS A total of 137 GB patients were included. Tumor volumes were assessed using semiautomatic 3D segmentation of contrast-enhancing and T2/FLAIR lesions on baseline and follow-up MRI. PFS was determined using RANO, mRANO, and RANO 2.0 criteria based on volumetric measurements. Spearman correlation was used to assess the relationship between PFS and OS. Pairwise differences in PFS between criteria were assessed using the Wilcoxon signed-rank test, with Bonferroni correction for multiple comparisons. Landmark analysis at 8 months and Cox regression were performed to evaluate the prognostic impact of progression. RESULTS Median PFS differed significantly across criteria: 9.14 months (RANO), 15.5 months (mRANO), and 14.2months (RANO 2.0). The highest correlation between PFS and OS was observed for mRANO (r = 0.70), followed by RANO 2.0 (r = 0.66) and RANO (r = 0.30). At the 8-month landmark, the hazard ratio (HR) for death in patients with progressive disease was highest for RANO 2.0 (HR = 3.6), compared to HR = 3.3 (mRANO), and HR = 2.6 (RANO). CONCLUSION In this cohort of newly diagnosed glioblastoma patients, both mRANO and RANO 2.0 demonstrated strong prognostic value. While mRANO showed the highest correlation between PFS and OS, RANO 2.0 identified progressive patients with the highest hazard of death at 8 months, underscoring its potential as a more powerful tool for early risk stratification.
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Heugenhauser et al. (2025) studied this question.
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