Development and Validation of a Nomogram for Predicting Overall Survival in Patients with Simultaneous Multiple Primary Lung Cancers after Surgery

Background Simultaneous multiple primary lung cancers (sMPLC) are detected increasingly nowadays, but there is no reliable tool for the assessment of long-term survival probability. This study aimed to develop a nomogram estimating the probability of the overall survivalfor patients with sMPLC undergoing resection . Methods This retrospective study enrolled 447 patients with sMPLC from two centers between June 2010 and April 2023. All patients were randomly divided in a 3:1 ratio to generate the training and validation cohorts. The least absolute shrinkage and selection operator regression analysis, and multivariable Cox regression analysis were utilized to identify risk factors for overall survival. Based on these factors, a nomogram for predicting overall survival was constructed. Areas under the receiver operating characteristic curves (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in both the training and validation cohorts. Results The training cohort included 335 patients, and the validation cohort included 112 patients, with a total of 64 (14.5%) patients occurring all-cause death. Age, pathological pattern, pleural invasion, neutrophile granulocyte, and nature of lesions wereused to build the nomogram. The AUC valuesof the nomogram were 0.855, 0.831 and 0.810 for the 1-, 3-, and 5-year overall survival in the training cohort, and 0.790, 0.778 and 0.786 in the validation cohort, respectively. The calibration curves for probability of overall survival showed optimal agreement between the prediction by nomogram and the actual observation, and the DCA indicated that the constructed nomogram conferred high clinical net benefit. Conclusion The nomogram demonstrated the ability to predict the likelihood of overall survival in patients with synchronous multiple primary lung cancer after surgical intervention, based on easily available indicators from electronic health records.