Abstract:
Objective To explore the prognostic value of metabolic parameters of primary and metastatic lesions in patients with advanced non-small cell lung cancer (NSCLC) who received first-line chemotherapy combined with immunotherapy before treatment.
Methods The clinical and baseline 18F-FDG PET/CT data of 86 patients with advanced lung cancer who received first-line chemotherapy combined with immunotherapy in the Affiliated Hospital of Qingdao University from December 2018 to September 2023 were retrospectively analyzed. The patients were evaluated after 4 cycles. The results were divided into clinical benefit group (including CR, PR and SD) and non-clinical benefit group (PD). The metabolic parameters of primary tumor (SUVmax-p, MTVp, TLG-p) and metastases (SUVmax-m, MTV-m, TLG-m) and their ratio (R-SUVmax, R-MTV, R-TLG) were obtained with 42%SUVmax as threshold. Multivariate logistic regression was used for multivariate analysis. Survival curves were constructed using the Kaplan-Meier method, and the log-rank test was used to compare the differences between subgroups. Univariate analysis P<0.05 was included in the Cox proportional hazard model to predict the prognostic factors of progression-free survival (PFS).
Results The median follow-up time was 26.5 months. 64 (74.42%) cases had disease progression, with a median PFS of 8 months (1-55 months). Among the 86 patients, 57(66.28%) cases in the clinical benefit group and 29 (33.72%) cases in the non-clinical benefit group. There were significant differences between clinical benefit group and non-clinical benefit group in clinical staging (0.018), MTV-p (0.004), TLG-p (0.006), R-SUVmax (0.045), R-MTV (0.022) and R-TLG (0.011). Cox analysis showed that the larger the R-MTV (HR=1.452, 95%CI: 1.111−1.897; P=0.006) and R-TLG (HR=0.860, 95%CI: 0.768−0.962; P=0.008), the shorter the survival time.
Conclusion Clinical stage and the ratio of MTV and TLG of baseline 18F-FDG PET/CT metastasis to primary lesions are independent risk factors for PFS in NSCLC patients, which can predict the efficacy of immunotherapy.