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

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.

Keywords: classification; computer tomography; differentiated thyroid cancer; magnetic resonance imaging; prediction; radiomics; ultrasound.

Figure 1

The flowchart shows the workflow of radiomics and its application in thyroid cancer or nodule classification and prediction. Abbreviations: RF—random forest; SVM—support vector machine

Figure 2

Flow diagram for the identification and exclusion of studies in radiomics application in differentiated thyroid cancer and nodules. Abbreviations: DTC—differentiated thyroid cancer; CAD—computer-aided detection; CNN—convolutional neural networks; AI—artificial intelligence.