Predictive Value of a Prognostic Model Based on Lymphocyte-to-Monocyte Ratio Before Radioiodine Therapy for Recurrence of Papillary Thyroid Carcinoma

Abstract

Background: The aim of this study was to investigate the predictive value of a prognostic model based on the lymphocyte-to-monocyte ratio (LMR) before radioiodine treatment for the recurrence of papillary thyroid carcinoma (PTC). Methods: Clinicopathological data of 441 patients with papillary thyroid cancer were collected retrospectively. The Receiver operating characteristic (ROC) was used to determine the optimal cut-off value for predicting PTC recurrence by LMR before radioiodine treatment. Recurrence was the endpoint of the study, and survival was estimated by the Kaplan-Meier method, and any differences in survival were evaluated with a stratified log-rank test. Univariate and multifactorial analyses were performed using Cox proportional-hazards models to identify risk factors associated with PTC recurrence. Results: The ROC curve showed that the best cut-off value of LMR before radioiodine treatment to predict recurrence in patients with PTC was 6.61, with a sensitivity of 54.1%, a specificity of 73%, and an area under the curve of 0.628. The recurrence rate was significantly higher in the low LMR group (16%) than in the high LMR group (5%) (P = 0.001, χ2 = 12.005). Multifactorial analysis showed that LMR < 6.61 (P = 0.006; HR = 2.508) and risk stratification (high risk) (P = 0.000; HR = 5.076) before radioiodine treatment were independent risk factors predicting recurrence in patients with PTC. Patients with preoperative LMR < 6.61 and high risk stratification had the lowest recurrence-free survival rate and the shortest recurrence-free survival time. Conclusions: The LMR-based prognostic model before radioactive iodine treatment is valuable for early prediction of PTC recurrence and it can be used in clinical practice as a supplement to risk stratification and applied in combination to help screen out patients with poorer prognosis early.