A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease

被引:14
作者
Lu, Wen-Wu [1 ]
Zhang, Di [2 ]
Ni, Xue-Jun [1 ]
机构
[1] Nantong Univ, Med Sch, Dept Med Ultrasound, Affiliated Hosp, Nantong, Jiangsu, Peoples R China
[2] Anhui Med Univ, Dept Ultrasound, Affiliated Hosp 1, Hefei, Anhui, Peoples R China
来源
MEDICAL SCIENCE MONITOR | 2022年 / 28卷
关键词
Artificial Intelligence; Thyroid Nodule; Ultrasonography; Doppler; FEATURES; CANCER; SEGMENTATION; ASSOCIATION; PREDICTION; CARCINOMA; DIAGNOSIS; MUTATION; NODULES; IMAGES;
D O I
10.12659/MSM.937738
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
The incidence of thyroid disease has gradually increased in recent years. Conventional ultrasound is one of the most critical thyroid imaging methods, but it still has certain limitations. The use of B-model ultrasound (BMUS) diagnosis of thyroid disease will be affected by a doctors' clinical experience. The ultrasound radiomics is based on ultrasound images to delineate the region of interest (ROI), and then extract features to quantify the disease information contained in the image, which helps to analyze the correlation between the image and the clinical pathology of the disease. By building a powerful model, it can be used to diagnose benign and ma-lignant thyroid nodules, predict lymph node status in thyroid cancer, analyze molecular biological characteris-tics, and predict the survival of thyroid cancer patients. At present, the application of ultrasound radiomics in the thyroid is pervasive. These ultrasound radiomics studies have further promoted the progress of ultrason-ic technology in the field of thyroid disease. Clinicians should be familiar with the workflow of ultrasound ra-diomics and understand the application of this technology to the thyroid. In this article, we first describe the workflow of ultrasound radiomics, followed by an overview of the application of ultrasound radiomics to the thyroid. Finally, some current limitations of the technology and areas for future improvement are discussed. This article aims to review the role of ultrasound radiomics and its application and limitations in the investiga-tion of thyroid disease.
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页数:8
相关论文
共 59 条
[1]  
Anawalt B, 2000, ENDOTEXT, P2
[2]   Machine and deep learning methods for radiomics [J].
Avanzo, Michele ;
Wei, Lise ;
Stancanello, Joseph ;
Vallieres, Martin ;
Rao, Arvind ;
Morin, Olivier ;
Mattonen, Sarah A. ;
El Naqa, Issam .
MEDICAL PHYSICS, 2020, 47 (05) :E185-E202
[3]   A Semi-Supervised Method for Tumor Segmentation in Mammogram Images [J].
Azary, Hanie ;
Abdoos, Monireh .
JOURNAL OF MEDICAL SIGNALS & SENSORS, 2020, 10 (01) :12-18
[4]   A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results [J].
Bagherzadeh-Khiabani, Farideh ;
Ramezankhani, Azra ;
Azizi, Fereidoun ;
Hadaegh, Farzad ;
Steyerberg, Ewout W. ;
Khalili, Davood .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2016, 71 :76-85
[5]   Radiomics of liver MRI predict metastases in mice [J].
Becker A.S. ;
Schneider M.A. ;
Wurnig M.C. ;
Wagner M. ;
Clavien P.A. ;
Boss A. .
European Radiology Experimental, 2 (1)
[6]  
Blum M, ULTRASONOGRAPHY THYR
[7]   Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy [J].
Chamming's, Foucauld ;
Ueno, Yoshiko ;
Ferre, Romuald ;
Kao, Ellen ;
Jannot, Anne-Sophie ;
Chong, Jaron ;
Omeroglu, Atilla ;
Mesurolle, Benoit ;
Reinhold, Caroline ;
Gallix, Benoit .
RADIOLOGY, 2018, 286 (02) :412-420
[8]   Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments [J].
Chang, Yongjun ;
Paul, Anjan Kumar ;
Kim, Namkug ;
Baek, Jung Hwan ;
Choi, Young Jun ;
Ha, Eun Ju ;
Lee, Kang Dae ;
Lee, Hyoung Shin ;
Shin, DaeSeock ;
Kim, Nakyoung .
MEDICAL PHYSICS, 2016, 43 (01) :554-567
[9]   An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules [J].
Chen, Yufan ;
Gao, Zixiong ;
He, Yanni ;
Mai, Wuping ;
Li, Jinhua ;
Zhou, Meijun ;
Li, Sushu ;
Yi, Wenhong ;
Wu, Shuyu ;
Bai, Tong ;
Zhang, Ning ;
Zeng, Weibo ;
Lu, Yao ;
Liu, Hongmei .
RADIOLOGY, 2022, 303 (03) :613-619
[10]  
Cheng Zixuan, 2019, Zhong Nan Da Xue Xue Bao Yi Xue Ban, V44, P244, DOI 10.11817/j.issn.1672-7347.2019.03.003