Prognostic factors and survival of recurrent glioblastoma: a systematic review

Introduction : Glioblastoma is a highly aggressive brain cancer with poor prognosis. Recurrence is common, and survival post-recurrence is limited. Identifying prognostic factors for recurrent glioblastoma can optimize treatment and improve outcomes. Aim : This systematic review analyzed the clinical, molecular, and treatment-related variables that influence survival in patients with recurrent glioblastoma. Materials and methods : A comprehensive search of PubMed, Scopus, and ProQuest databases included studies from the past decade, assessed using the Newcastle-Ottawa Scale (NOS). Results : Sixteen studies were analyzed, highlighting age, Karnofsky Performance Status (KPS), molecular markers (MGMT promoter methylation, IDH mutations, TERT promoter mutations, TP53 alterations, ATRX loss, and Ki-67 expression), and surgical resection extent as key prognostic factors. Younger patients with higher KPS scores and favorable molecular markers had better survival. Molecular profiling and maximal resection correlated with improved overall survival (OS). Salvage therapies like chemotherapy and re-resection provided marginal benefits, with variability based on patient demographics and tumor genetics. Conclusion : Age, KPS, molecular markers, and surgical resection extent significantly predict survival in recurrent glioblastoma. The review underscores the importance of molecular profiling for personalized treatment, though current salvage therapies show limited effectiveness. Innovative approaches are needed to enhance outcomes for this aggressive disease. Abbreviations used in the article: BSC : best supportive care; CRE : complete resection of enhancing tumor; DFS : disease-free survival; GBM : glioblastoma; GTR : gross total resection; KPS : Karnofsky Performance Status; NOS : Newcastle-Ottawa Scale; OS : overall survival; PFS : progression-free survival; rGBM : recurrent glioblastoma multiforme; RTOG–RPA : Radiation Therapy Oncology Group – Recursive Partitioning Analysis; TTF : tumor-treating fields