Optimal Therapies for Recurrent Glioblastoma: A Bayesian Network Meta-Analysis

Area of Science:

• Neuro-oncology
• Clinical pharmacology
• Biostatistics

Background:

• Recurrent glioblastoma (GBM) treatment remains controversial, necessitating comparative efficacy analysis of active therapies.
• Identifying optimal treatment strategies is crucial for improving patient outcomes in recurrent GBM.
• Existing evidence requires synthesis to rank available therapeutic options.

Approach:

• A systematic review and Bayesian network meta-analysis were conducted to compare and rank active therapies for recurrent GBM.
• Treatment hierarchy was established using Surface Under the Cumulative Ranking Curve (SUCRA) and mean ranks.
• Cluster analysis aggregated results from progression-free survival, objective response rate, and overall survival outcomes.

Key Points:

• Bevacizumab (BEV) plus lomustine (CCNU) and regorafenib demonstrated superior efficacy in objective response rate (ORR), 6-month progression-free survival (6-m PFS), and overall survival (OS).
• BEV monotherapy or combination therapies showed advantages in ORR and 6-m PFS, particularly beneficial for patients with severe neurological symptoms.
• Cediranib plus CCNU was associated with the highest rates of grade 3-4 adverse events, highlighting safety considerations.

Conclusions:

• Bevacizumab (BEV) plus CCNU and regorafenib are primary recommendations for recurrent GBM treatment based on efficacy and ranking analyses.
• BEV monotherapy or combination therapies are advised for patients experiencing severe neurological symptoms.
• Further investigation into advanced therapies like tumor treatment fields (TTF) and immunotherapy is warranted for future recurrent GBM management.