Qiao Tingting, Cheng Zhaoping, Duan Yanhua
Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Graduate School, Shandong First Medical University, Jinan, China.
Front Oncol. 2025 Jan 20;14:1530507. doi: 10.3389/fonc.2024.1530507. eCollection 2024.
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.
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.
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.
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.
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.
多参数正电子发射断层扫描（PET）成像与多组学数据的整合在预测癌症免疫治疗疗效方面已显示出巨大的临床潜力。然而，其具体的预测能力和潜在机制仍不清楚。
本综述系统评估多参数PET成像指标（如最大标准化摄取值（SUVmax）、代谢肿瘤体积（MTV）和总病变糖酵解（TLG））在预测免疫治疗疗效（包括PD-1/PD-L1抑制剂和嵌合抗原受体T细胞（CAR-T）疗法）中的应用，并探讨其与多组学数据整合时在提高预测准确性方面的潜在作用。
对PubMed、Embase和Web of Science数据库进行系统检索，以确定使用纵向PET/CT数据和实体瘤疗效评价标准（RECIST）或免疫治疗疗效评价标准（iRECIST）评估免疫治疗疗效的研究。仅纳入原始的前瞻性或回顾性研究进行分析。查阅综述文章和荟萃分析以获取更多参考文献，但排除在定量分析之外。排除缺乏标准化疗效评估的研究，以确保数据的完整性和质量。
多参数PET成像指标对各种免疫治疗的疗效具有较高的预测能力。SUVmax、MTV和TLG等代谢参数与治疗反应率、无进展生存期（PFS）和总生存期（OS）显著相关。多组学数据（包括基因组学和蛋白质组学）与PET成像的整合提高了疗效预测的敏感性和准确性。通过综合分析，PET代谢参数在预测免疫治疗反应模式（如假性进展和超进展）方面显示出潜力。
多参数PET成像与多组学数据的整合在预测免疫治疗疗效方面具有广阔的潜力，并可能支持个性化治疗策略的制定。未来需要使用大规模、多中心数据集进行验证，以进一步推动癌症免疫治疗中的精准医学发展。
