Detailed characterization of metastatic lymph nodes improves the prediction accuracy of currently used risk stratification systems in N1 stage papillary thyroid cancer. Issue 1 (July 2020)

• Record Type: Journal Article
• Authors: Lee, Jandee; Kim, Chan Hee; Min, In Kyung; Jeong, Seonhyang; Kim, Hyunji; Choi, Moon Jung; Kwon, Hyeong Ju; Jung, Sang Geun; Jo, Young Suk
• Is Part Of: European journal of endocrinology. Volume 183:Issue 1(2020)
• Page Start: 83
• Page End: 93
• Publication Date: 2020-07
• Subjects: Endocrinology -- Periodicals; 616.4005
• DOI: 10.1530/EJE-20-0131
• Languages: English
• ISSNs: 0804-4643

Abstract

Abstract : Objective: The characteristics of metastatic lymph nodes (MLNs) have been investigated as important predictors of recurrence and progression in papillary thyroid cancer (PTC). However, clinically applicable risk stratification systems are limited to the assessment of size and number of MLNs. This study investigated the predictive value of detailed characteristics of MLNs in combination with currently used risk stratification systems. Design and methods: We retrospectively characterized 2811 MLNs from 9014 harvested LNs of 286 patients with N1 PTC according to the maximum diameter of MLN (MDLN), maximum diameter of metastatic focus (MDMF), ratio of both diameters (MDMFR), lymph node ratio (LNR, number of MLNs/number of total harvested LNs), presence of extranodal extension (ENE), desmoplastic reaction (DR), cystic component, and psammoma body. Results: Factors related to the size and number of MLNs were associated with increased risk of recurrence and progression. Extensive presence of ENE (>40%) and DR (≥50%) increased the risk of recurrence/progression. The combination of MDLN, LNR, ENE, and DR had the highest predictive value among MLN characteristics. Combination of these parameters with ATA risk stratification or 1-year response to therapy improved the predictive power for recurrence/progression from a Harrell's C-index of 0.781 to 0.936 and 0.867 to 0.960, respectively. Conclusions: The combination of currently used risk stratification systems with detailed characterization of MLNs may improve the predictive accuracy for recurrence/progression in N1 PTC patients.