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- W3161408898 abstract "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." @default.
- W3161408898 created "2021-05-24" @default.
- W3161408898 creator A5022903030 @default.
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- W3161408898 creator A5043989533 @default.
- W3161408898 creator A5062972242 @default.
- W3161408898 creator A5089505260 @default.
- W3161408898 date "2021-05-18" @default.
- W3161408898 modified "2023-10-16" @default.
- W3161408898 title "Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations" @default.
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- W3161408898 doi "https://doi.org/10.3390/cancers13102436" @default.
- W3161408898 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8157383" @default.
- W3161408898 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34069887" @default.