Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients.

Background: Most patients with advanced non-small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan. We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients. This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients. Methods: Multiple machine-learning models were used in a retrospective cohort of 6586 patients. First, we modeled and validated a nomogram to predict the overall survival of NSCLC patients. Subsequently, five machine-learning models (logistic regression, random forest, XGBoost, decision tree, and light gradient boosting machine) were used to predict survival status. Next, we evaluated the performance of the models. Finally, the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure: time-dependent prediction accuracy. Results: Among the five machine-learning models, the accuracy of random forest model outperformed the others. Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months, the prediction accuracies of both the nomogram and machine-learning models changed as time varied. The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month, and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month. Conclusions: Overall, the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period. Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies, the conventional nomogram was competitive. In real clinical applications, a comprehensive model that combines these two methods may demonstrate superior capabilities.