Common laboratory parameters as predictors of prognosis in primary lung cancer - Cai, Mingchun; Chen, Hao; Yan, Zhengbo; He, Xuehua (Frontiers in Oncology, 2026)
BackgroundThe prognosis of patients with primary lung cancer remains poor. Therefore, this study aimed to develop and validate a predictive model to evaluate the overall survival (OS) of these patients.MethodsA retrospective analysis was conducted on the data of 1,308 patients with primary lung cancer who received treatment and follow-up at our hospital from 2016 to 2022. The entire cohort was randomly divided into a derivation cohort (70%, n=915) and a validation cohort (30%, n=393) in a 7:3 ratio. A prognostic nomogram was constructed using Cox-least absolute shrinkage and selection operator regression analysis to predict the OS probabilities at 1-, 3-, and 5-years. Kaplan–Meier curve and log-rank tests were used to analyze and compare OS among different patient subgroups. The model was comprehensively evaluated through the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).ResultsAge, gender, red blood cell count, serum potassium, albumin-globulin ratio, and prothrombin time activity were the prognostic indicators for predicting OS in patients with primary lung cancer. In the derivation cohort, the AUCs at 1-, 3-, and 5-years were 0.739 (95% confidence interval [CI]: 0.702–0.776), 0.727 (95% CI: 0.690–0.764), and 0.675 (95% CI: 0.629–0.721). In the validation cohort, the AUCs at 1-, 3-, and 5-years were 0.770 (95% CI: 0.712–0.827), 0.784 (95% CI: 0.732–0.837), and 0.717 (95% CI: 0.646–0.789), respectively. The calibration curve and DCA results confirmed the model’s good predictive power.ConclusionIn this study, we developed and validated an OS prediction model for patients with primary lung cancer. Providing personalized predictions with multiple outcomes increases the information available to patients and clinicians.