Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations

Simple Summary Differentiated thyroid cancer (DTC) is the most common endocrine malignancy with a high incidence rate in females. The COVID-19 epidemic posed an increased risk of treatment delay causing increased DTC morbidity and mortality rate of DTC. Several imaging techniques, including ultrasound (US), magnetic resonance imaging (MRI), and computer tomography (CT), have been applied in the early screening and diagnosis of DTC. However, these traditional methods have limited sensitivity and specificity due to dependence on the experience and skill of the radiologists. 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.