Prognostic Value of 18F-FDG PET/CT Metabolic Parameters in Resectable Non–small Cell Lung Cancer Treated With Neoadjuvant Immunotherapy Plus Chemotherapy

Objective: This study investigates the predictive value of 18 F-FDG PET/CT metabolic parameters in patients with non–small cell lung cancer (NSCLC) undergoing neoadjuvant immunotherapy plus chemotherapy. Methods: We conducted a retrospective analysis of clinical data from 131 patients with pathologically confirmed NSCLC who were deemed resectable after 3 cycles of neoadjuvant immunotherapy plus chemotherapy. Pretreatment and post-treatment PET metabolic parameters were evaluated. CT assessments based on immune response evaluation criteria in solid tumors (iRECIST) were compared with PET/CT assessments using the response criteria in solid tumors (PERCIST). ROC curve analysis and Kaplan-Meier survival analysis, including univariate and Cox multivariate analyses, were employed to assess the prognostic value of PET metabolic parameters after treatment. Results: The PET/CT assessment based on PERCIST showed high consistency with prognosis, while the CT assessment based on iRECIST demonstrated low consistency. Statistically significant differences were observed between the iRECIST and PERCIST criteria ( P <0.001). ROC curve analysis revealed significant differences in post-treatment PET metabolic parameters (postSUVmax, postSUVmean, postSUVpeak, postMTV, and postTLG) as well as the percentage changes in metabolic parameters before and after treatment(Δ) (ΔSUVmax, ΔSUVmean, ΔSUVpeak, ΔMTV, and ΔTLG) ( P <0.05). Optimal cutoff values enabled stratification into high-risk and low-risk groups. Univariate analysis showed significantly higher survival in the low-risk group for all parameters except ΔMTV ( P =0.311), while Cox multivariate analysis identified ΔSUVmax as the most predictive. Conclusions: The PERCIST is more accurate than iRECIST in evaluating prognosis for NSCLC neoadjuvant immunotherapy plus chemotherapy. PET metabolic parameters, particularly ΔSUVmax, effectively predict prognosis and support clinical decision-making.