Detailed characterization of metastatic lymph nodes improves the prediction accuracy of currently used risk stratification systems in N1 stage papillary thyroid cancer

Jandee Lee, Chan Hee Kim, In Kyung Min, Seonhyang Jeong, Hyunji Kim, Moon Jung Choi, Hyeong Ju Kwon, Sang Geun Jung, Young Suk Jo

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)83-93
Number of pages11
JournalEuropean Journal of Endocrinology
Volume183
Issue number1
DOIs
Publication statusPublished - 2020 Jun

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

Fingerprint Dive into the research topics of 'Detailed characterization of metastatic lymph nodes improves the prediction accuracy of currently used risk stratification systems in N1 stage papillary thyroid cancer'. Together they form a unique fingerprint.

  • Cite this