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Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma

This study aimed to establish a model for predicting lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. A total of 106 patients with papillary thyroid carcinoma undergoing initial treatment of neck lymph node dissection (including central and lateral neck lymph nodes) a...

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Autores principales: Jin, Shan, Bao, Wuyuntu, Yang, Yun-Tian, Bai, Tala, Bai, Yinbao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255803/
https://www.ncbi.nlm.nih.gov/pubmed/30478394
http://dx.doi.org/10.1038/s41598-018-35551-9
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author Jin, Shan
Bao, Wuyuntu
Yang, Yun-Tian
Bai, Tala
Bai, Yinbao
author_facet Jin, Shan
Bao, Wuyuntu
Yang, Yun-Tian
Bai, Tala
Bai, Yinbao
author_sort Jin, Shan
collection PubMed
description This study aimed to establish a model for predicting lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. A total of 106 patients with papillary thyroid carcinoma undergoing initial treatment of neck lymph node dissection (including central and lateral neck lymph nodes) at the thyroid surgery department were enrolled from January 2009 to April 2017. Logistic regression analysis was used to screen the factors influencing lateral neck lymph node metastasis and develop a prediction model. The receiver operating characteristic curve was used to evaluate the predictive power and boundary value of the model for lateral neck lymph node metastasis. Prediction model: Logistic(P) = −5.699 + 0.681 × ((TSH)) + 0.342 × ((Metastatic rate of central lymph nodes)) + 1.463 × ((Combined with Hashimoto’s disease)) + 1.525 × ((Number of tumors)). When logistic (P) was ≥ 0.821, it was predicted that lateral neck lymph node metastasis occurred in patients with papillary thyroid carcinoma. When logistic (P) was <0.821, it was predicted that no metastasis was found in the lateral neck lymph node. The prediction accuracy was 78.3%. The model helped in evaluating lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. Also, it had significance in determining reasonable surgical range, reducing unnecessary lateral neck lymph node dissection, and further improving the quality of life of patients.
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spelling pubmed-62558032018-12-03 Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma Jin, Shan Bao, Wuyuntu Yang, Yun-Tian Bai, Tala Bai, Yinbao Sci Rep Article This study aimed to establish a model for predicting lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. A total of 106 patients with papillary thyroid carcinoma undergoing initial treatment of neck lymph node dissection (including central and lateral neck lymph nodes) at the thyroid surgery department were enrolled from January 2009 to April 2017. Logistic regression analysis was used to screen the factors influencing lateral neck lymph node metastasis and develop a prediction model. The receiver operating characteristic curve was used to evaluate the predictive power and boundary value of the model for lateral neck lymph node metastasis. Prediction model: Logistic(P) = −5.699 + 0.681 × ((TSH)) + 0.342 × ((Metastatic rate of central lymph nodes)) + 1.463 × ((Combined with Hashimoto’s disease)) + 1.525 × ((Number of tumors)). When logistic (P) was ≥ 0.821, it was predicted that lateral neck lymph node metastasis occurred in patients with papillary thyroid carcinoma. When logistic (P) was <0.821, it was predicted that no metastasis was found in the lateral neck lymph node. The prediction accuracy was 78.3%. The model helped in evaluating lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. Also, it had significance in determining reasonable surgical range, reducing unnecessary lateral neck lymph node dissection, and further improving the quality of life of patients. Nature Publishing Group UK 2018-11-26 /pmc/articles/PMC6255803/ /pubmed/30478394 http://dx.doi.org/10.1038/s41598-018-35551-9 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Jin, Shan
Bao, Wuyuntu
Yang, Yun-Tian
Bai, Tala
Bai, Yinbao
Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
title Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
title_full Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
title_fullStr Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
title_full_unstemmed Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
title_short Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
title_sort establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255803/
https://www.ncbi.nlm.nih.gov/pubmed/30478394
http://dx.doi.org/10.1038/s41598-018-35551-9
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