The Evolving Role of Nuclear Medicine in Differentiating Pseudoprogression from Tumor Progression in Gliomas
Synopsis
This review focuses on studies published over the past 18 months and examines the role of molecular PET imaging in differentiating treatment-related changes, especially pseudoprogression, from true tumor progression in gliomas, suggesting that static amino acid PET, particularly [18F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocols, that [68Ga]FAPI PET may add information on the tumor microenvironment, and that quantitative PET parameters are affected by reconstruction algorithms, reference regions and segmentation strategies, with artificial intelligence, radiomics and automated segmentation potentially improving the integration of multimodal and quantitative assessment.
Interpretation
Static amino acid PET, particularly [18F]FET, may provide diagnostic information comparable to dynamic acquisition while simplifying protocol acquisition. Relative to complex dynamic protocols, this direction suggests that a simpler acquisition workflow may yield comparable diagnostic information. The review notes that this evidence remains heterogeneous and largely retrospective, without reporting specific sample sizes or effect sizes.
[68Ga]FAPI PET may provide complementary information on the tumor microenvironment and represents an emerging research direction. Beyond amino acid tracers, it introduces a tracer targeting the tumor microenvironment as an additional information dimension. It is framed as an emerging research direction without specific diagnostic performance data.
Increasing use of quantitative PET parameters highlights the impact of reconstruction algorithms, reference regions and segmentation strategies on quantitative biomarkers and diagnostic thresholds. It explicitly identifies standardization of quantitative assessment as a key factor affecting diagnostic thresholds. Based on synthesis of recent literature, without specific comparative values.
Artificial intelligence, radiomics and automated segmentation may further improve the integration of multimodal and quantitative assessment. It positions AI-based approaches as potential tools for integrating multimodal and quantitative information rather than as standalone diagnostic conclusions. A directional judgment offered by the review, still requiring prospective multicenter validation.
Perspective
The scope of this review is response assessment after glioma treatment, particularly when MRI struggles to distinguish treatment-related changes from true progression; its value lies in pointing to three actionable directions for clinical practice and research: simpler acquisition protocols, standardization of quantitative assessment, and integration of complementary biological information. For researchers and nuclear medicine teams seeking to optimize PET acquisition workflows or establish quantitative interpretation standards, these directions offer concrete points of focus.
Readers should still watch whether the comparability of static and dynamic amino acid PET is reproducibly confirmed in prospective multicenter studies; whether the diagnostic value of emerging tracers such as [68Ga]FAPI can be validated; how differences in reconstruction algorithms, reference regions and segmentation strategies affect the consistency of diagnostic thresholds; and whether the optimal timing of repeated PET and the clinical utility of AI approaches ultimately translate into patient outcomes. In addition, the loaded text is an incomplete version lacking figures and reference details, so the design and numerical results of individual studies cannot be further summarized.