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

Author:

Lee Jandee1,Kim Chan Hee2,Min In Kyung3,Jeong Seonhyang4,Kim Hyunji1,Choi Moon Jung1,Kwon Hyeong Ju5,Jung Sang Geun6,Jo Young Suk47

Affiliation:

1. 1Department of Surgery, Open NBI Convergence Technology Research Laboratory, Severance Hospital, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea

2. 2Yonsei University College of Medicine, Seoul, South Korea

3. 3Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea

4. 4Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea

5. 5Department of Pathology, Yonsei University, Wonju College of Medicine, Wonju, South Korea

6. 6Department of Gynecological Oncology, Bundang CHA Medical Center, CHA University, Gyeonggi-do, South Korea

7. 7Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, South Korea

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.

Publisher

Bioscientifica

Subject

Endocrinology,General Medicine,Endocrinology, Diabetes and Metabolism

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3