Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND: Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC). METHODS: We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients. RESULTS: We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions. CONCLUSION: We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.