The evolving role of nuclear medicine in differentiating pseudoprogression from tumor progression in gliomas

Purpose of review Gliomas remain associated with poor outcomes, despite multimodal treatment. Response assessment relies primarily on MRI; however, treatment-related changes (TRC), particularly pseudoprogression (psPD), may mimic tumor progression disease (PD), creating a major diagnostic challenge. This review focuses on studies published over the past 18 months addressing the role of molecular PET imaging in differentiating TRC from PD in gliomas, highlighting developments with potential implications for clinical practice. Recent findings Recent evidence emphasizes the increasingly complementary role of molecular PET in glioma response assessment. Static amino acid PET, particularly [ 18 F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocol acquisition, although evidence remains heterogeneous and retrospective. Beyond amino acid tracers, [ 68 Ga]FAPI PET may provide complementary information on tumor microenvironment and presents an emerging research direction. Increasing use of quantitative PET parameters has also highlighted the impact of reconstruction algorithms, reference regions and segmentation strategies on quantitative biomarkers and diagnostic thresholds. Artificial intelligence, radiomics and automated segmentation may further improve the integration of multimodal and quantitative assessment. Summary Molecular PET imaging provides clinically relevant complementary information to conventional MRI in the assessment of TRC, including psPD. Recent evidence highlights three complementary directions: simpler acquisition protocols, greater standardization of quantitative assessment and integration of complementary biological information. Future prospective multicenter studies are needed to establish the optimal timing and role of repeated PET examinations, validate emerging tracers, quantitative biomarkers and artificial intelligence based approaches, determining their impact on patient outcomes.