Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer

Serum enzymes, blood cytology indices, and pathological features are associated with the prognosis of patients with lung cancer, and we construct prognostic prediction models based on clinicopathological indices in patients with resectable lung cancer. The study includes 420 patients with primary lung cancer who underwent pneumonectomy. Cox proportional hazards regression was conducted to analyze the prognostic values of individual clinicopathological indices. The prediction accuracies of models for overall survival (OS) and progression-free survival (PFS) were estimated through Harrell's concordance indices (C-index) and Brier scores. Nomograms of the prognostic models were plotted for individualized evaluations of death and cancer progression. We find that the prognostic model based on alkaline phosphatase (ALP), lactate dehydrogenase (LDH), age, history of tuberculosis, and pathological stage present exceptional performance for OS prediction [C-index: 0.74 (95% CI, 0.69-0.79) and Brier score: 0.10], and the prognostic model based on ALP, LDH, and platelet distribution width (PDW), age, pathological stage, and histological type presented outstanding performance for PFS prediction [C-index: 0.71 (95% CI, 0.66-0.75) and Brier score: 0.18]. These findings show that the models based on clinicopathological indices might serve as economic and efficient prognostic tools for resectable lung cancer.