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

Abstract

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. We aimed to build a new nomogram and a random survival forest model using machine learning to predict the risk of LSCC progress. 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. The ROC curve revealed that the Cox model exhibited good sensitivity and specificity in predicting DFS in the training and validation cohorts (1 year, validation AUC = 0.679, training AUC = 0.693; 3 years, validation AUC = 0.716, training AUC = 0.655; 5 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. 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.

Figure 1

(A) Kaplan–Meier curves pertaining to disease-free survival (DFS). (B) Forest plot of DFS. (C) Nomogram of DFS.

Figure 2

(A,B) The area under the curve (AUC) for Cox regression model predicting the DFS of training cohort. (C,D) the area under the curve (AUC) for Cox regression model predicting the DFS of validation cohort. (E) Calibration curve for 3-year DFS.

Figure 3

(A) VIMP of clinical factors in random survival forest model. (B) Kaplan-Meier survival curves of the high and low risk groups in training sets (P < 0.0001). (C) Kaplan-Meier survival curves of the high and low risk groups in validation sets (P = 0.0.024). (D) The ROC curve of predicting DFS in the training cohort. (E) The ROC curve of predicting DFS in the validation cohort.