Optimal Cut-Off Values of Lymph Node Ratio Predicting Recurrence in Papillary Thyroid Cancer

• Published in: Medicine (Baltimore) Vol. 95; no. 5; p. e2692
• Main Authors: Lee, Seul Gi, Ho, Joon, Choi, Jung Bum, Kim, Tae Hyung, Kim, Min Jhi, Ban, Eun Jeong, Lee, Cho Rok, Kang, Sang-Wook, Jeong, Jong Ju, Nam, Kee-Hyun, Jung, Sang Geun, Jo, Young Suk, Lee, Jandee, Chung, Woong Youn
• Format: Journal Article
• Language: English
• Published: United States The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved 01-02-2016; Wolters Kluwer Health
• Subjects: Adolescent; Adult; Aged; Carcinoma - epidemiology; Carcinoma - pathology; Carcinoma, Papillary; Female; Humans; Lymph Nodes - pathology; Lymphatic Metastasis; Male; Middle Aged; Neoplasm Recurrence, Local - epidemiology; Observational Study; Predictive Value of Tests; Republic of Korea - epidemiology; Retrospective Studies; Thyroid Cancer, Papillary; Thyroid Neoplasms - epidemiology; Thyroid Neoplasms - pathology; Young Adult; Republic of Korea
• ISSN: 0025-7974; 1536-5964
• DOI: 10.1097/MD.0000000000002692

Regional lymph node (LN) metastasis has a significant impact for prediction of recurrence in patients with papillary thyroid cancers (PTC); however, the prognostic value of the lymph node ratio (LNR), which is defined as the ratio of the number of metastatic LNs to the total number of investigated LNs, is controversial. In this study, we determined the optimal cut-off values of LNRs for the prediction of recurrence in PTC patients.This large cohort study retrospectively evaluated 2294 patients who had undergone total thyroidectomy for PTC at a single institution from October 1985 to June 2009. The prediction probability of central LNR (cLNR, level VI) and total LNR (tLNR, levels II-VI) were estimated by binominal logistic regression analysis. Hazard ratios of the cut-off LNR values for cancer recurrence were calculated for relevant covariates using multivariate Cox regression analyses. Kaplan-Meier analyses were also utilized to assess the effects of estimated LNR cut-off values on recurrence-free survival (RFS).Of the 2294 patients, 138 (6.0%) presented cancer recurrence during the follow-up period (median duration = 107.1 months). The prediction probability indicated that LNRs of 0.4 and 0.5 for central LN and total LN, respectively, are optimal cut-off values for precise prediction with minimization of outliers. Multivariate Cox regression analyses revealed that cLNR ≥0.4 was independently predictive of recurrence in patients with N0 and N1a PTCs (hazard ratio [HR]: 7.016, 95% confidence interval [CI]: 3.72-12.986, P < 0.001) and that tLNR ≥0.5 indicated a significantly increased risk of recurrence in patients with N1b PTCs (HR: 2.372, 95% CI: 1.458-3.860, P < 0.001). In addition, Kaplan-Meier analyses clearly demonstrated that these LNR cut-off values are precisely operational in RFS estimation.The cut-off LNR values of 0.4 and 0.5 for cLNR and tLNR, respectively, were identified. Risk stratification combined with these LNR cut-off values may prove useful to determine treatment and follow-up strategies for PTC patients.