Cargando…

Diagnostic value of multimodal ultrasound imaging in differentiating benign and malignant TI-RADS category 4 nodules

BACKGROUND: Differential diagnosis of benign and malignant thyroid imaging reporting and data system category 4 (TI-RADS-4) nodules can be difficult using conventional ultrasound (US). This study aimed to evaluate whether multimodal ultrasound imaging can improve differentiation and characterization...

Descripción completa

Detalles Bibliográficos
Autores principales:	Pei, Shufang, Cong, Shuzhen, Zhang, Bin, Liang, Changhong, Zhang, Lu, Liu, Juanjuan, Guo, Yuping, Zhang, Shuixing
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Singapore 2019
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525125/ https://www.ncbi.nlm.nih.gov/pubmed/30825007 http://dx.doi.org/10.1007/s10147-019-01397-y

Ejemplares similares

Ultrasound Real-Time Tissue Elastography Improves the Diagnostic Performance of the ACR Thyroid Imaging Reporting and Data System in Differentiating Malignant from Benign Thyroid Nodules: A Summary of 1525 Thyroid Nodules
por: Pei, Shufang, et al.
Publicado: (2020)

Usefulness of combined use of contrast-enhanced ultrasound and TI-RADS classification for the differentiation of benign from malignant lesions of thyroid nodules
por: Zhang, Yan, et al.
Publicado: (2016)

Radiomics Score Combined with ACR TI-RADS in Discriminating Benign and Malignant Thyroid Nodules Based on Ultrasound Images: A Retrospective Study
por: Luo, Peng, et al.
Publicado: (2021)

Diagnostic performance of simplified TI-RADS for malignant thyroid nodules: comparison with 2017 ACR-TI-RADS and 2020 C-TI-RADS
por: Chen, Zhiguang, et al.
Publicado: (2022)

The value of TI-RADS combined with superb micro-vascular imagine in distinguishing benign and malignant thyroid nodules: A meta-analysis
por: Zhu, Changfu, et al.
Publicado: (2022)

Contrast-enhanced ultrasound and shear wave elastography in the diagnosis of ACR TI-RADS 4 and 5 category thyroid nodules coexisting with Hashimoto’s thyroiditis
por: Wang, Bin, et al.
Publicado: (2023)

Contrast-Enhanced Ultrasound in the Differential Diagnosis and Risk Stratification of ACR TI-RADS Category 4 and 5 Thyroid Nodules With Non-Hypovascular
por: Wang, Yanfang, et al.
Publicado: (2021)

Diagnostic Performance of ACR and Kwak TI-RADS for Benign and Malignant Thyroid Nodules: An Update Systematic Review and Meta-Analysis
por: Kang, Yun Jin, et al.
Publicado: (2022)

Likelihood of malignancy in thyroid nodules according to a proposed Thyroid Imaging Reporting and Data System (TI-RADS) classification merging suspicious and benign ultrasound features
por: Delfim, Ricardo Luiz Costantin, et al.
Publicado: (2017)

Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS
por: Lu, Wenwu, et al.
Publicado: (2022)

Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience
por: Jin, Zhuang, et al.
Publicado: (2020)

The use of modified TI-RADS using contrast-enhanced ultrasound features for classification purposes in the differential diagnosis of benign and malignant thyroid nodules: A prospective and multi-center study
por: Zhou, Ping, et al.
Publicado: (2023)

Does a three-degree hypoechogenicity grading improve ultrasound thyroid nodule risk stratification and affect the TI-RADS 4 category? A retrospective observational study
por: Delfim, Ricardo Luiz Costantin, et al.
Publicado: (2023)

Explainable Automated TI-RADS Evaluation of Thyroid Nodules
por: Kunapinun, Alisa, et al.
Publicado: (2023)

Comparison of Diagnostic Performance of Five Different Ultrasound TI-RADS Classification Guidelines for Thyroid Nodules
por: Yang, Ruoning, et al.
Publicado: (2020)

High Risk Thyroid Nodule Discrimination and Management by Modified TI-RADS
por: Li, Menghui, et al.
Publicado: (2021)

Pulmonary Multiple Nodules: Benign or Malignant?
por: Liu, Jing, et al.
Publicado: (2018)

Clinical diagnostic value of contrast-enhanced ultrasound and TI-RADS classification for benign and malignant thyroid tumors: One comparative cohort study
por: Xu, Yan, et al.
Publicado: (2019)

B-mode Ultrasound Characteristics of Thyroid Nodules With High-Benign Probability and Nodules With Risk of Malignancy
por: Sultan, Salahaden R
Publicado: (2023)

Reliability of Thyroid Imaging Reporting and Data System (TI-RADS), and ultrasonographic classification of the American Thyroid Association (ATA) in differentiating benign from malignant thyroid nodules
por: de Macedo, Bruno Mussoi, et al.
Publicado: (2018)

Diagnostic value of ACR TI-RADS combined with three-dimensional shear wave elastography in ACR TI-RADS 4 and 5 thyroid nodules
por: Hao, Lijun, et al.
Publicado: (2023)

Application of Real-time Ultrasound Elastography in Diagnosing Benign and Malignant Thyroid Solid Nodules
por: Wang, Hai-ling, et al.
Publicado: (2012)

Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning
por: Liang, Ting, et al.
Publicado: (2023)

Improvement of diagnostic efficiency in distinguishing the benign and malignant thyroid nodules via conventional ultrasound combined with ultrasound contrast and elastography
por: Liu, Mei-Juan, et al.
Publicado: (2017)

Utility of gray-scale ultrasound to differentiate benign from malignant thyroid nodules
por: Popli, Manju Bala, et al.
Publicado: (2012)

Using Ultrasound-Based Multilayer Perceptron to Differentiate Early Breast Mucinous Cancer and its Subtypes From Fibroadenoma
por: Liang, Ting, et al.
Publicado: (2021)

Evaluation of malignancy with thyroid imaging reporting and data system (TI-RADS) in thyroid nodules with persistent nondiagnostic cytology
por: BAŞER, Hüsniye, et al.
Publicado: (2019)

Tips for improving consistency of thyroid nodule interpretation with ACR TI-RADS
por: Low, Gavin, et al.
Publicado: (2022)

Thyroid hemiagenesis with a TI-RADS 2 nodule in the contralateral lobe
por: Sereke, Senai Goitom, et al.
Publicado: (2021)

Diagnostic performance of the EU TI‐RADS and ACR TI‐RADS scoring systems in predicting thyroid malignancy
por: Chatti, Hiba‐Allah, et al.
Publicado: (2023)

Predictors of Malignancy in Thyroid Nodules Classified as Bethesda Category III
por: Liu, Xiaoli, et al.
Publicado: (2022)

Diagnostic performance of a new two-dimensional shear wave elastography expression using siemens ultrasound system combined with ACR TI-RADS for classification of benign and malignant thyroid nodules: A prospective multi-center study
por: Qi, Wei-Hong, et al.
Publicado: (2023)

Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis
por: Zhu, Pei-Shan, et al.
Publicado: (2022)

Role of Duplex Power Doppler Ultrasound in Differentiation between Malignant and Benign Thyroid Nodules
por: Algin, Oktay, et al.
Publicado: (2010)

Application of Texture Analysis Method for Classification of Benign and Malignant Thyroid Nodules in Ultrasound Images
por: Abbasian Ardakani, Ali, et al.
Publicado: (2015)

Value of differential diagnosis of contrast-enhanced ultrasound in benign and malignant thyroid nodules with microcalcification
por: Gao, Zhixiang, et al.
Publicado: (2019)

Identification of benign and malignant thyroid nodules based on dynamic AI ultrasound intelligent auxiliary diagnosis system
por: Wang, Bing, et al.
Publicado: (2022)

Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study
por: Jin, Pengfei, et al.
Publicado: (2023)

Malignancy rate of Bethesda category III thyroid nodules according to ultrasound risk stratification system and cytological subtype
por: Yoo, Won Sang, et al.
Publicado: (2020)

Multimodal ultrasound fusion network for differentiating between benign and malignant solid renal tumors
por: Zhu, Dongmei, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_rk7vjljo3b6lfq1k6vopq6rf6c