Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

ObjectivesTo apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging...

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Bibliographic Details
Main Authors: Jia-hui Chen, Yu-Qing Zhang, Tian-tong Zhu, Qian Zhang, Ao-xue Zhao, Ying Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Endocrinology
Subjects:
thyroid nodules
ultrasound
contrast-enhanced ultrasound
machine learning
radiomics features
key frames
Online Access:https://www.frontiersin.org/articles/10.3389/fendo.2024.1299686/full

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