Development of a predictive model for recurrence in postoperative glottic laryngeal squamous cell carcinoma patients following adjuvant chemotherapy based on PNI, NLR, and PLR

Abstract

Objective: To identify key factors influencing postoperative recurrence in patients with glottic laryngeal squamous cell carcinoma (LSCC) and to develop a predictive model incorporating traditional clinicopathological features and novel inflammatory and immune indicators. This model aims to provide a theoretical foundation for individualized prediction of postoperative recurrence risk and support clinical decision-making.

Methods: Clinical and laboratory data were collected from 614 patients with glottic laryngeal cancer who underwent surgery between April 2010 and December 2021. The study included inflammatory and immune-related indicators (such as NLR, PLR, PNI, IL-6, IL-8), alongside traditional clinical features like age, T stage, lymph node metastasis, and degree of differentiation. Univariate and multivariate logistic regression, as well as Cox regression analyses, were performed to identify factors associated with recurrence. A Nomogram model was constructed based on Cox regression results. The model's predictive performance was evaluated using ROC curves, the concordance index (C-index), and calibration curves, with validation conducted in both training and validation cohorts.

Results: Multivariate analysis identified age, T stage, lymph node metastasis, degree of differentiation, IL-6, IL-8, PNI, and PLR as independent factors influencing postoperative recurrence in patients with glottic laryngeal cancer. The Nomogram model demonstrated excellent predictive performance in both the training and validation cohorts, with AUCs for 12-, 24-, and 36-month recurrence-free survival predictions of 0.887, 0.906, and 0.915 (training cohort) and 0.895, 0.906, and 0.907 (validation cohort), respectively. The model's concordance indices were 0.860 and 0.857 in the training and validation groups, respectively. Calibration curves revealed a high degree of agreement between predicted and actual outcomes.

Conclusion: The Nomogram model developed in this study integrates multiple clinical and inflammatory-immune indicators, enabling accurate prediction of 12-, 24-, and 36-month recurrence-free survival rates in post-surgical patients with glottic laryngeal cancer. The model holds significant clinical value, with IL-6, IL-8, and PNI identified as crucial indicators for predicting recurrence risk, providing valuable insights for postoperative follow-up and individualized treatment strategies.

Keywords: Glottic laryngeal cancer; inflammatory markers; nomogram model; postoperative recurrence; prognostic nutritional index (PNI).

Figure 1

Flow chart of the study sample screening. LSCC, laryngeal squamous cell carcinoma.

Figure 2

Comparison of baseline characteristics of patients between the recurrence and non-recurrence groups. Note: BMI, Body mass index; T stage, pathological T stage; LNM, lymph node metastasis.

Figure 3

Receiver Operating Characteristic (ROC) curve analysis and cut-off values for laboratory indicators predicting postoperative recurrence in glottic laryngeal carcinoma. A. ROC curve for Alb in predicting postoperative recurrence. B. ROC curve for Lym in predicting postoperative recurrence. C. ROC curve for Neu in predicting postoperative recurrence. D. ROC curve for PLT in predicting postoperative recurrence. E. ROC curve for IL-6 in predicting postoperative recurrence. F. ROC curve for IL-8 in predicting postoperative recurrence. G. ROC curve for PNI in predicting postoperative recurrence. H. ROC curve for NLR in predicting postoperative recurrence. I. ROC curve for PLR in predicting postoperative recurrence. Note: Alb, Albumin; Lym, lymphocyte count; Neu, neutrophil count; PLT, platelet count; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; IL-6, interleukin-6; IL-8, interleukin-8.

Figure 4

Spearman correlation matrix heatmap of variables after assignment. Note: Alb, Albumin; Lym, lymphocyte count; Neu, neutrophil count; PLT, platelet count; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; IL-6, interleukin-6; IL-8, interleukin-8; Recurrence, recurrence.

Figure 5

Kaplan-Meier survival curves of significant predictors for recurrence-free survival. A. Kaplan-Meier survival curve for recurrence-free survival stratified by age. B. Kaplan-Meier survival curve for recurrence-free survival stratified by gender. C. Kaplan-Meier survival curve for smoking history and recurrence-free survival stratified by. D. Kaplan-Meier survival curve for recurrence-free survival stratified by T-stage. E. Kaplan-Meier survival curve for recurrence-free survival stratified by lymph node metastasis. F. Kaplan-Meier survival curve for recurrence-free survival stratified by degree of differentiation. G. Kaplan-Meier survival curve for recurrence-free survival stratified by postoperative chemotherapy. H. Kaplan-Meier survival curve for recurrence-free survival stratified by IL-6. I. Kaplan-Meier survival curve for recurrence-free survival stratified by PNI. J. Kaplan-Meier survival curve for recurrence-free survival stratified by PLR. Note: HR, Hazard ratio; PNI, Prognostic Nutritional Index ; NLR, Neutrophil-to-Lymphocyte Ratio; PLR, Platelet-to-Lymphocyte Ratio; IL-6, Interleukin-6; IL-8, Interleukin-8; T stage, T stage.

Figure 6

Nomogram model constructed based on independent factors selected from multivariate cox regression analysis.

Figure 7

Performance evaluation of the nomogram model in the training and validation cohorts. A. Kaplan-Meier survival curve of RiskScore in the training cohort. B. ROC curve for 12-month, 24-month, and 36-month survival prediction in the training cohort. C. Calibration curve for 12-month, 24-month, and 36-month survival prediction in the training cohort. D. Kaplan-Meier survival curve of RiskScore in the validation cohort. E. ROC curve for 12-month, 24-month, and 36-month survival prediction in the validation cohort. F. Calibration curve for 12-month, 24-month, and 36-month survival prediction in the validation cohort. Note: AUC, Area under the curve; C-index, concordance index; ROC, receiver operating characteristic; K-M, Kaplan-Meier.