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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1109319/full |
| _version_ | 1827997330986500096 |
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| author | XiaoFan Gao Xuan Ran Wei Ding |
| author_facet | XiaoFan Gao Xuan Ran Wei Ding |
| author_sort | XiaoFan Gao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T05:26:53Z |
| format | Article |
| id | doaj.art-5e2bb9b731364112a1d4a79bfc72d032 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T05:26:53Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-5e2bb9b731364112a1d4a79bfc72d0322023-03-07T14:27:48ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.11093191109319The progress of radiomics in thyroid nodulesXiaoFan GaoXuan RanWei DingDue 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1109319/fullradiomicsthyroid nodulesmachine learningartificial intelligencePTC |
| spellingShingle | XiaoFan Gao Xuan Ran Wei Ding The progress of radiomics in thyroid nodules Frontiers in Oncology radiomics thyroid nodules machine learning artificial intelligence PTC |
| 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 | radiomics thyroid nodules machine learning artificial intelligence PTC |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1109319/full |
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