Frontiers in Surgery (Jan 2023)

A prediction model for the 5-year, 10-year and 20-year mortality of medullary thyroid carcinoma patients based on lymph node ratio and other predictors

  • Yanhua An,
  • Jingkai Lu,
  • Mosheng Hu,
  • Qiumei Cao

DOI
https://doi.org/10.3389/fsurg.2022.1044971
Journal volume & issue
Vol. 9

Abstract

Read online

AimTo explore the predictive value of lymph node ratio (LNR) for the prognosis of medullary thyroid carcinoma (MTC) patients, and constructed prediction models for the 5-year, 10-year and 20-year mortality of MTC patients based on LNR and other predictors.MethodsThis cohort study extracted the data of 2,093 MTC patients aged ≥18 years undergoing total thyroidectomy and neck lymph nodes dissection. Kaplan-Meier curves and log-rank tests were performed to compare survival curves between LNR < 15% group and LNR ≥ 15% group. All data was divided into the training set (n = 1,465) and the testing set (n = 628). The random survival forest model was constructed in the training set and validated in the testing set. The area under the curve (AUC) was employed for evaluating the predictive ability of the model.ResultsThe 5-year, 10-year and 20-year overall survival (OS) and cause-specific survival (CSS) of MTC patients with LNR <15% were higher than those with LNR ≥15%. The OS was 46% and the CSS was 75% after 20 years' follow-up. The AUC of the model for the 5-year, 10-year, and 20-year OS in MTC patients was 0.878 (95%CI: 0.856–0.900), 0.859 (95%CI: 0.838–0.879) and 0.843 (95%CI: 0.823–0.862) in the training set and 0.845 (95%CI: 0.807–0.883), 0.841 (95%CI: 0.807–0.875) and 0.841 (95%CI: 0.811–0.872) in the testing set. In the training set, the AUCs were 0.869 (95%CI: 0.845–0.892), 0.843 (95%CI: 0.821–0.865), 0.819 (95%CI: 0.798–0.840) for the 5-year, 10-year and 20-year CCS in MTC patients, respectively. In the testing set, the AUCs were 0.857 (95%CI: 0.822–0.892), 0.839 (95%CI: 0.805–0.873) and 0.826 (95%CI: 0.794–0.857) for the 5-year CCS, 10-year CCS and 20-year CCS in MTC patients, respectively.ConclusionThe models displayed good predictive performance, which might help identify MTC patients might have poor outcomes and appropriate interventions should be applied in these patients.

Keywords