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

in European Journal of Endocrinology
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  • 1 Department of Surgery, Open NBI Convergence Technology Research Laboratory, Severance Hospital, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
  • 2 Yonsei University College of Medicine, Seoul, South Korea
  • 3 Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
  • 4 Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
  • 5 Department of Pathology, Yonsei University, Wonju College of Medicine, Wonju, South Korea
  • 6 Department of Gynecological Oncology, Bundang CHA Medical Center, CHA University, Gyeonggi-do, South Korea
  • 7 Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, South Korea

Correspondence should be addressed to S G Jung or Y S Jo; Email: sgoncol@chamc.co.kr or joys@yuhs.ac

*(J Lee and C H Kim contributed equally to this work)

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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.

Supplementary Materials

    • Supplementary Table 1. Characteristics of the Study Patients (N = 286)
    • Supplementary Table 2. Comparison of Clinicopathological Features in Patients With N1 Stage PTC between Occult Metastatic LNs and Clinically Detected Metastatic LNs (N=286)
    • Supplementary Table 3. Cox Regression Analysis of Recurrence/Progression Using ATA Risk Stratification or 1-Year Response to Therapy With or Without the Best Combination of Metastatic LN Characteristics

 

     European Society of Endocrinology

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