Predicting survival of advanced laryngeal squamous cell carcinoma: Comparison of machine learning models and Cox regression models

Abstract

Background:Laryngeal squamous cell carcinoma (LSCC) is a common tumor type. High recurrence rates remain an important factor affecting the survival and quality of life of advanced LSCC patients. Objective:We aimed to build a new nomogram and a random survival forest model using machine learning to predict the risk of LSCC progress. Material and Methods: The study included 671 patients with AJCC stages III–IV LSCC. To develop a prognostic model, Cox regression analyses were used to assess the relationship between clinic-pathologic factors and disease-free survival (DFS). RSF analysis was also used to predict the DFS of LSCC patients. Results:The ROC curve revealed that the Cox model exhibited good sensitivity and specificity in predicting DFS in the training and validation cohorts (one year, validation AUC = 0.679, training AUC = 0.693; three years, validation AUC = 0.716, training AUC = 0.655; five years, validation AUC = 0.717, training AUC = 0.659). Random survival forest analysis showed that N stage, clinical stage, and postoperative chemoradiotherapy were prognostically significant variables associated with survival. Conclusions: The random forest model exhibited better prediction ability than the Cox regression model in the training cohort; however, the two models showed similar prediction ability in the validation cohort.