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

Abstract

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. 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. 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. 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.