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The progress of radiomics in thyroid nodules

Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits an...

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Detalles Bibliográficos
Autores principales: Gao, XiaoFan, Ran, Xuan, Ding, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029726/
https://www.ncbi.nlm.nih.gov/pubmed/36959790
http://dx.doi.org/10.3389/fonc.2023.1109319
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author Gao, XiaoFan
Ran, Xuan
Ding, Wei
author_facet Gao, XiaoFan
Ran, Xuan
Ding, Wei
author_sort Gao, XiaoFan
collection PubMed
description Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits and significantly enhancing the ability of medical imaging identification and prediction. The literature indicates that radiomics has a high potential in identifying and predicting thyroid nodules. So in this article, we explain the development, definition, and workflow of radiomics. And then, we summarize the applications of various imaging techniques in identifying benign and malignant thyroid nodules, predicting invasiveness and metastasis of thyroid lymph nodes, forecasting the prognosis of thyroid malignancies, and some new advances in molecular level and deep learning. The shortcomings of this technique are also summarized, and future development prospects are provided.
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spelling pubmed-100297262023-03-22 The progress of radiomics in thyroid nodules Gao, XiaoFan Ran, Xuan Ding, Wei Front Oncol Oncology Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits and significantly enhancing the ability of medical imaging identification and prediction. The literature indicates that radiomics has a high potential in identifying and predicting thyroid nodules. So in this article, we explain the development, definition, and workflow of radiomics. And then, we summarize the applications of various imaging techniques in identifying benign and malignant thyroid nodules, predicting invasiveness and metastasis of thyroid lymph nodes, forecasting the prognosis of thyroid malignancies, and some new advances in molecular level and deep learning. The shortcomings of this technique are also summarized, and future development prospects are provided. Frontiers Media S.A. 2023-03-07 /pmc/articles/PMC10029726/ /pubmed/36959790 http://dx.doi.org/10.3389/fonc.2023.1109319 Text en Copyright © 2023 Gao, Ran and Ding 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
Gao, XiaoFan
Ran, Xuan
Ding, Wei
The progress of radiomics in thyroid nodules
title The progress of radiomics in thyroid nodules
title_full The progress of radiomics in thyroid nodules
title_fullStr The progress of radiomics in thyroid nodules
title_full_unstemmed The progress of radiomics in thyroid nodules
title_short The progress of radiomics in thyroid nodules
title_sort progress of radiomics in thyroid nodules
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029726/
https://www.ncbi.nlm.nih.gov/pubmed/36959790
http://dx.doi.org/10.3389/fonc.2023.1109319
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