Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/10/2436 |
| _version_ | 1797533706328473600 |
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| author | Yuan Cao Xiao Zhong Wei Diao Jingshi Mu Yue Cheng Zhiyun Jia |
| author_facet | Yuan Cao Xiao Zhong Wei Diao Jingshi Mu Yue Cheng Zhiyun Jia |
| author_sort | Yuan Cao |
| collection | DOAJ |
| description | Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients. |
| first_indexed | 2024-03-10T11:19:26Z |
| format | Article |
| id | doaj.art-cb717f0743c14bcda7d7d37336252907 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T11:19:26Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-cb717f0743c14bcda7d7d373362529072023-11-21T20:12:22ZengMDPI AGCancers2072-66942021-05-011310243610.3390/cancers13102436Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and LimitationsYuan Cao0Xiao Zhong1Wei Diao2Jingshi Mu3Yue Cheng4Zhiyun Jia5Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, ChinaDepartment of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, ChinaDepartment of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, ChinaDepartment of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, ChinaDepartment of Radiology, West China Hospital of Sichuan University, Chengdu 610040, ChinaDepartment of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, ChinaRadiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.https://www.mdpi.com/2072-6694/13/10/2436differentiated thyroid cancerradiomicsultrasoundmagnetic resonance imagingcomputer tomographyprediction |
| spellingShingle | Yuan Cao Xiao Zhong Wei Diao Jingshi Mu Yue Cheng Zhiyun Jia Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations Cancers differentiated thyroid cancer radiomics ultrasound magnetic resonance imaging computer tomography prediction |
| title | Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations |
| title_full | Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations |
| title_fullStr | Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations |
| title_full_unstemmed | Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations |
| title_short | Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations |
| title_sort | radiomics in differentiated thyroid cancer and nodules explorations application and limitations |
| topic | differentiated thyroid cancer radiomics ultrasound magnetic resonance imaging computer tomography prediction |
| url | https://www.mdpi.com/2072-6694/13/10/2436 |
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