[Construction of a prognostic nomogram combining PET/CT metabolic parameters and blood inflammatory markers for non-small cell lung cancer treated with first-line chemotherapy].

Objective: To investigate the prognostic value of F-FDG-PET/CT metabolic parameters and blood inflammatory markers for advanced non-small cell lung cancer (NSCLC, stage Ⅳ/ⅢB) treated with first-line chemotherapy combined with immunotherapy and construct a nomogram prediction model for NSCLC.

Methods: We retrospectively analyzed the metabolic parameters (SUVmax, MTV and TLG) and blood markers of inflammation (NLR, DNLR, PLR and SII) in 105 patients with advanced NSCLC receiving chemotherapy combined with baseline F-FDG-PET/CT prior to immunotherapy from March, 2019 to June, 2021. ROC curve was used to calculate the best cut-off points for grouping, and univariate and multivariate COX regression analyses were performed to screen the independent predictors of prognosis for a combined diagnostic analysis. The effective biomarkers were included in the prediction model, and the nomogram model was constructed using the cph function in the rms function package of R language software.

Results: The patients were followed up for a median of 17.5 months, and their median progression-free survival (PFS) was 16 months with a median overall survival (OS) of 13.6 months. A high PLR (≥151.050) and a high TLG (≥101.940) were significant independent prognostic factors for PFS, and a high SII (≥941.385) and a high TLG (≥101.940) were independent prognostic factors for OS. The nomogram combining PET and blood markers of inflammation showed a good performance for prognostic prediction (with C-index of 0.682 for PFS and of 0.727 for OS) and good fitting of the calibration curve. The clinical decision curve showed good clinical utility of the nomogram.

Conclusion: The baseline PET/CT metabolic parameters and blood inflammatory markers are associated with PFS and OS of patients with advanced NSCLC receiving first-line chemotherapy, and the constructed nomogram based on these parameters has a good performance for prognostic prediction in these patients.