Development and validation of a population-based model for predicting the regional lymph node metastasis in adolescent differentiated thyroid carcinoma. (October 2021)

• Record Type: Journal Article
• Authors: Min, Yu; Xiang, Ke; Feng, Yang; Chen, Hang; Chen, Jialin; Wei, Xiaoyuan; Yin, Guobing
• Is Part Of: Oral oncology. Volume 121(2021)
• Publication Date: 2021-10
• Subjects: Adolescent -- Differentiated thyroid carcinoma -- Lymph node metastasis -- SEER -- Nomogram; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2021.105507
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: This is large population-based study on 1, 930 adolescents. We identified five clinical factors in promoting regional lymph node metastasis. We established the first predictive model for this subpopulation. Our nomograms showed good discrimination with a C-index of 0.774, 0.746, and 0.732. Models could help surgeons make better clinical decisions in lymph node dissection. Abstract: Background: Adolescent differentiated thyroid carcinoma (DTC) is a rare type of thyroid cancer that represents a special entity of all endocrine-related cancer. This study aims to establish the first nomogram for predicting the regional (central and lateral) lymph node metastasis (LNM) in the adolescent population for better surgical management. Method: We retrospectively reviewed the clinicopathology characteristics of adolescent patients with DTC in the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Results: A total of 1, 930 adolescent patients between the ages of 10 and 24 years from the SEER database were enrolled in this study. Six predictive factors including age, race, histology, multifocality, extrathyroidal invasion (EI) and tumor size were identified to be significantly associated with the regional LNM via univariate and multivariate logistic regression analyses. These indicators were used to construct a nomogram for predicting the regional LNM in adolescent patients with DTC. Moreover, a satisfied predictive ability of the model was determined with a C-index of 0.794, supported by an internal validation group with a C-index of 0.776. The Decision Curve Analysis and calibration curve further conducted a great agreement in our model. Conclusion: The first predictive model containing multiple factors has been successfully established with good discrimination for predicting the regional LNM in adolescent patients with DTC. This nomogram could effectively help surgeons to make better individualized surgical decision intraoperatively, especially in terms of whether cervical lymph node dissection (LND) is warranted.