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The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics
SIMPLE SUMMARY: Pediatric thyroid ultrasound-guided fine-needle aspiration includes the use of dedicated ultrasound guidance algorithms to improve the accuracy and increase the diagnostic yield of the procedure. The refinement of imaging criteria and risk stratification can potentially spare cytolog...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093758/ https://www.ncbi.nlm.nih.gov/pubmed/37046700 http://dx.doi.org/10.3390/cancers15072039 |
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author | Seminati, Davide Ceola, Stefano Pincelli, Angela Ida Leni, Davide Gatti, Andrea Garancini, Mattia L’Imperio, Vincenzo Cattoni, Alessandro Pagni, Fabio |
author_facet | Seminati, Davide Ceola, Stefano Pincelli, Angela Ida Leni, Davide Gatti, Andrea Garancini, Mattia L’Imperio, Vincenzo Cattoni, Alessandro Pagni, Fabio |
author_sort | Seminati, Davide |
collection | PubMed |
description | SIMPLE SUMMARY: Pediatric thyroid ultrasound-guided fine-needle aspiration includes the use of dedicated ultrasound guidance algorithms to improve the accuracy and increase the diagnostic yield of the procedure. The refinement of imaging criteria and risk stratification can potentially spare cytological assessment of benign nodules. Molecular testing can also be used to differentiate benign from malignant lesions and to guide their further management. The assistance of artificial intelligence tools will be helpful in this investigation. ABSTRACT: Thyroid fine-needle aspiration (FNA) is a commonly used diagnostic cytological procedure in pediatric patients for the evaluation of thyroid nodules, triaging them for the detection of thyroid cancer. In recent years, greater attention has been paid to thyroid FNA in this setting, including the use of updated ultrasound score algorithms to improve accuracy and yield, especially considering the theoretically higher risk of malignancy of these lesions compared with the adult population, as well as to minimize patient discomfort. Moreover, molecular genetic testing for thyroid disease is an expanding field of research that could aid in distinguishing benign from cancerous nodules and assist in determining their clinical management. Finally, artificial intelligence tools can help in this task by performing a comprehensive analysis of all the obtained data. These advancements have led to greater reliance on FNA as a first-line diagnostic tool for pediatric thyroid disease. This review article provides an overview of these recent developments and their impact on the diagnosis and management of thyroid nodules in children. |
format | Online Article Text |
id | pubmed-10093758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100937582023-04-13 The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics Seminati, Davide Ceola, Stefano Pincelli, Angela Ida Leni, Davide Gatti, Andrea Garancini, Mattia L’Imperio, Vincenzo Cattoni, Alessandro Pagni, Fabio Cancers (Basel) Review SIMPLE SUMMARY: Pediatric thyroid ultrasound-guided fine-needle aspiration includes the use of dedicated ultrasound guidance algorithms to improve the accuracy and increase the diagnostic yield of the procedure. The refinement of imaging criteria and risk stratification can potentially spare cytological assessment of benign nodules. Molecular testing can also be used to differentiate benign from malignant lesions and to guide their further management. The assistance of artificial intelligence tools will be helpful in this investigation. ABSTRACT: Thyroid fine-needle aspiration (FNA) is a commonly used diagnostic cytological procedure in pediatric patients for the evaluation of thyroid nodules, triaging them for the detection of thyroid cancer. In recent years, greater attention has been paid to thyroid FNA in this setting, including the use of updated ultrasound score algorithms to improve accuracy and yield, especially considering the theoretically higher risk of malignancy of these lesions compared with the adult population, as well as to minimize patient discomfort. Moreover, molecular genetic testing for thyroid disease is an expanding field of research that could aid in distinguishing benign from cancerous nodules and assist in determining their clinical management. Finally, artificial intelligence tools can help in this task by performing a comprehensive analysis of all the obtained data. These advancements have led to greater reliance on FNA as a first-line diagnostic tool for pediatric thyroid disease. This review article provides an overview of these recent developments and their impact on the diagnosis and management of thyroid nodules in children. MDPI 2023-03-29 /pmc/articles/PMC10093758/ /pubmed/37046700 http://dx.doi.org/10.3390/cancers15072039 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Seminati, Davide Ceola, Stefano Pincelli, Angela Ida Leni, Davide Gatti, Andrea Garancini, Mattia L’Imperio, Vincenzo Cattoni, Alessandro Pagni, Fabio The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics |
title | The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics |
title_full | The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics |
title_fullStr | The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics |
title_full_unstemmed | The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics |
title_short | The Complex Cyto-Molecular Landscape of Thyroid Nodules in Pediatrics |
title_sort | complex cyto-molecular landscape of thyroid nodules in pediatrics |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093758/ https://www.ncbi.nlm.nih.gov/pubmed/37046700 http://dx.doi.org/10.3390/cancers15072039 |
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