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

Abstract

To investigate the prognostic value of 18F-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. 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 18F-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. 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. 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.