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A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population

OBJECTIVE: This study aims to investigate the diagnostic value of eight risk stratification systems (RSSs) for thyroid nodules in the elderly and explore the reasons in comparison with a younger group. METHODS: Cases of thyroid nodules that underwent ultrasound examination with thyroidectomy or fine...

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Autores principales: Ma, Xiao, Yu, Jing, Huang, Yuanjing, Cui, Yiyang, Cui, Kefei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684914/
https://www.ncbi.nlm.nih.gov/pubmed/38033487
http://dx.doi.org/10.3389/fonc.2023.1265973
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author Ma, Xiao
Yu, Jing
Huang, Yuanjing
Cui, Yiyang
Cui, Kefei
author_facet Ma, Xiao
Yu, Jing
Huang, Yuanjing
Cui, Yiyang
Cui, Kefei
author_sort Ma, Xiao
collection PubMed
description OBJECTIVE: This study aims to investigate the diagnostic value of eight risk stratification systems (RSSs) for thyroid nodules in the elderly and explore the reasons in comparison with a younger group. METHODS: Cases of thyroid nodules that underwent ultrasound examination with thyroidectomy or fine-needle aspiration (FNA) at our hospital between August 2013 and March 2023 were collected. The patients were categorized into two groups: an elderly group (aged ≥60) and a younger group (aged <60). Eight RSSs were applied to evaluate these nodules respectively. RESULTS: The malignant rate in the elderly group was significantly lower than that in the younger group (28.2% vs. 49.6%, P=0.000). There were statistically significant differences in nodule diameter, multiplicity, composition, echogenicity, orientation, margin, and echogenic foci between the elderly and younger groups (P<0.05). Among the eight RSSs evaluated in elderly adults, the artificial intelligence-based Thyroid Imaging Reporting and Data System (AI TIRADS) demonstrated the highest overall diagnostic efficacy, but with relatively high unnecessary FNA rate (UFR) and missed cancer rate (MCR) of 55.0% and 51.3%, respectively. By modifying the size thresholds, the new AI TI-RADS achieved the lowest UFR and MCR while maintaining nearly the lowest FNA rate (FNAR) among all the RSSs (P=0.172, 0.162, compared to the ACR and original AI, respectively, but P<0.05 compared to the other six RSSs). CONCLUSION: Among the eight RSS systems, AI demonstrated higher diagnostic efficacy in the elderly population. However, the size thresholds for FNA needed to be adjusted.
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spelling pubmed-106849142023-11-30 A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population Ma, Xiao Yu, Jing Huang, Yuanjing Cui, Yiyang Cui, Kefei Front Oncol Oncology OBJECTIVE: This study aims to investigate the diagnostic value of eight risk stratification systems (RSSs) for thyroid nodules in the elderly and explore the reasons in comparison with a younger group. METHODS: Cases of thyroid nodules that underwent ultrasound examination with thyroidectomy or fine-needle aspiration (FNA) at our hospital between August 2013 and March 2023 were collected. The patients were categorized into two groups: an elderly group (aged ≥60) and a younger group (aged <60). Eight RSSs were applied to evaluate these nodules respectively. RESULTS: The malignant rate in the elderly group was significantly lower than that in the younger group (28.2% vs. 49.6%, P=0.000). There were statistically significant differences in nodule diameter, multiplicity, composition, echogenicity, orientation, margin, and echogenic foci between the elderly and younger groups (P<0.05). Among the eight RSSs evaluated in elderly adults, the artificial intelligence-based Thyroid Imaging Reporting and Data System (AI TIRADS) demonstrated the highest overall diagnostic efficacy, but with relatively high unnecessary FNA rate (UFR) and missed cancer rate (MCR) of 55.0% and 51.3%, respectively. By modifying the size thresholds, the new AI TI-RADS achieved the lowest UFR and MCR while maintaining nearly the lowest FNA rate (FNAR) among all the RSSs (P=0.172, 0.162, compared to the ACR and original AI, respectively, but P<0.05 compared to the other six RSSs). CONCLUSION: Among the eight RSS systems, AI demonstrated higher diagnostic efficacy in the elderly population. However, the size thresholds for FNA needed to be adjusted. Frontiers Media S.A. 2023-11-15 /pmc/articles/PMC10684914/ /pubmed/38033487 http://dx.doi.org/10.3389/fonc.2023.1265973 Text en Copyright © 2023 Ma, Yu, Huang, Cui and Cui https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Ma, Xiao
Yu, Jing
Huang, Yuanjing
Cui, Yiyang
Cui, Kefei
A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
title A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
title_full A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
title_fullStr A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
title_full_unstemmed A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
title_short A comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
title_sort comprehensive comparative assessment of eight risk stratification systems for thyroid nodules in the elderly population
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684914/
https://www.ncbi.nlm.nih.gov/pubmed/38033487
http://dx.doi.org/10.3389/fonc.2023.1265973
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