Abstract BACKGROUND Pseudoprogression (PP) is a treatment-related imaging phenomenon that mimics true progression (P) after chemoradiotherapy for glioblastoma (GBM). Distinguishing between PP and P is essential to avoid premature therapy changes, yet reliable clinical predictors remain scarce. In this study, we investigated the occurrence, predictors, and survival impact of PP at multiple timepoints in a large, single-center cohort of GBM patients. MATERIAL AND METHODS In this retrospective study, we analyzed 215 adults with GBM who received surgical resection at the Medical University of Vienna between 2012 and 2024. Radiological outcomes were evaluated at three timepoints after radiotherapy completion: 6 months (M6), 9 months (M9), and 12 months (M12), followed by patient classification into PP, P or no radiological event. Clinical, imaging, and molecular variables were evaluated using univariate and multivariate logistic regression. Survival was analyzed with Kaplan-Meier curves and multivariate Cox regression models. RESULTS PP occurred in 9.3% of patients at 6 months, 11.2% at 9 months, and 10.2% at 12 months. MGMT promoter methylation was consistently associated with a higher likelihood of PP at all timepoints (M6: OR 4.72, p = 0.0115; M9: OR 4.58, p = 0.0045; M12: OR 3.79, p = 0.0138). CCNU use appeared linked to pseudoprogression, although the small sample size limits definitive conclusions. No reliable predictors emerged when comparing PP patients to those without any radiological changes. Patients who developed pseudoprogression survived longer than those with true progression (median 31.8 months vs. 13.4-16.4 months; p 0.0001). The survival rates of patients with PP matched those of patients who did not experience any radiological events. MGMT promoter methylation remained an independent predictor of improved survival across all timepoints. CONCLUSION Pseudoprogression is strongly associated with MGMT promoter methylation and favorable prognosis during the first 12 months after completion of radiotherapy. The integration of molecular data with continuous imaging assessments serves as a critical tool for making treatment choices improving patient outcomes.
Makolli et al. (Wed,) studied this question.