Predictive factors for lymph node metastasis in papillary thyroid cancer patients undergoing neck dissection: insights from a large cohort study

Front Oncol. 2024 Oct 25:14:1447903. doi: 10.3389/fonc.2024.1447903. eCollection 2024.

Abstract

Background: This study aimed to investigate the risk factors and metastatic patterns in papillary thyroid cancer (PTC) patients undergoing lymph node dissection, offering guidance for clinical practice.

Methods: A total of 924 PTC patients who underwent thyroidectomy with central neck dissection (CND) or lateral neck dissection (LND) between January 2021 and November 2022 were included in the analysis. The study investigated the relationships between clinicopathological characteristics, lymph node metastasis, and various risk factor.

Results: Among the 924 PTC patients, the cervical lymph node metastasis rate was 59.1% (546 patients). Of these patients, 381 had central neck metastasis (CNM, 41.2%), while the remaining 165 patients had lateral neck metastasis (LNM, 17.9%). Factors associated with increased risk of CNM and LNM included larger tumor diameter, presence of multiple tumors, and capsular invasion (p<0.05). Male sex, age <55 years, larger tumor diameter (>0.85 cm), multiple tumors, capsular invasion, and absence of Hashimoto's disease were identified as independent risk factors for CNM (p<0.05), with an AUC value of 0.722. CNM, maximum diameter >1.15 cm, and multiple tumors were independent risk factors for LNM (p<0.05), with an AUC of 0.699.

Conclusion: These findings suggest that tailored neck dissection based on individual risk factors is crucial, particularly in cases of suspected LNM with larger tumors, CNM, multiple tumors, and capsular invasion.

Keywords: lymph node; metastasis; neck dissection; risk factors; thyroid cancer.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Startup Fund for Scientific Research, Fujian Medical University (Grant number: 2020QH1228), the National Natural Science Foundation of China (81872169, 82172821, 82103386) and the Tianjin Municipal Science and Technology Project (19JCYBJC27400, 21JCZDJC00360).