The Future of Thyroid Nodule Risk Stratification

Abstract

Clinical evidence supports the association of ultrasound features with benign or malignant thyroid nodules and serves as the basis for sonographic stratification of thyroid nodules, according to an estimated thyroid cancer risk. Contemporary guidelines recommend management strategies according to thyroid cancer risk, thyroid nodule size, and the clinical scenario. Yet, reproducible and accurate thyroid nodule risk stratification requires expertise, time, and understanding of the weight different ultrasound features have on thyroid cancer risk. The application of artificial intelligence to overcome these limitations is promising and has the potential to improve the care of patients with thyroid nodules.

Keywords: Artificial intelligence; Risk stratification; Thyroid cancer; Thyroid nodules.

Copyright © 2021 Elsevier Inc. All rights reserved.

Figures

Figure 1.

Structure of conventional machine learning and deep learning (according to human or computer model extraction of the images or pixels) for thyroid nodule risk stratification. Supervised machine learning as during development classification/prediction of thyroid cancer/benign is provided to the model.

Figure 2.

Potential applications of AI to improve thyroid nodule risk stratifications, areas that need to be addressed in future studies in order to achieve the goal of improved clinical outcomes.