Innovative applications and future trends of multiparametric PET in the assessment of immunotherapy efficacy.

Background: The integration of multiparametric PET (Positron Emission Tomography.) imaging and multi-omics data has demonstrated significant clinical potential in predicting the efficacy of cancer immunotherapies. However, the specific predictive power and underlying mechanisms remain unclear.

Objective: This review systematically evaluates the application of multiparametric PET imaging metrics (e.g., SUVmax [Maximum Standardized Uptake Value], MTV [Metabolic Tumor Volume], and TLG [Total Lesion Glycolysis]) in predicting the efficacy of immunotherapies, including PD-1/PD-L1 inhibitors and CAR-T therapy, and explores their potential role in improving predictive accuracy when integrated with multi-omics data.

Methods: A systematic search of PubMed, Embase, and Web of Science databases identified studies evaluating the efficacy of immunotherapy using longitudinal PET/CT data and RECIST or iRECIST criteria. Only original prospective or retrospective studies were included for analysis. Review articles and meta-analyses were consulted for additional references but excluded from quantitative analysis. Studies lacking standardized efficacy evaluations were excluded to ensure data integrity and quality.

Results: Multiparametric PET imaging metrics exhibited high predictive capability for efficacy across various immunotherapies. Metabolic parameters such as SUVmax, MTV, and TLG were significantly correlated with treatment response rates, progression-free survival (PFS), and overall survival (OS). The integration of multi-omics data (including genomics and proteomics) with PET imaging enhanced the sensitivity and accuracy of efficacy prediction. Through integrated analysis, PET metabolic parameters demonstrated potential in predicting immune therapy response patterns, such as pseudo-progression and hyper-progression.

Conclusion: The integration of multiparametric PET imaging and multi-omics data holds broad potential for predicting the efficacy of immunotherapies and may support the development of personalized treatment strategies. Future validation using large-scale, multicenter datasets is needed to further advance precision medicine in cancer immunotherapy.