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

Overview

Journal Med Sci Monit

Specialties General Medicine
Pathology

Date 2022 Oct 19

PMID 36258648

Authors

Wen-Wu Lu

Di Zhang

Xue-Jun Ni

Affiliations

Soon will be listed here.

Abstract

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 malignant thyroid nodules, predict lymph node status in thyroid cancer, analyze molecular biological characteristics, 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 ultrasonic technology in the field of thyroid disease. Clinicians should be familiar with the workflow of ultrasound radiomics 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 investigation of thyroid disease.

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References

Gillies R, Schabath M . Radiomics Improves Cancer Screening and Early Detection. Cancer Epidemiol Biomarkers Prev. 2020; 29(12):2556-2567. DOI: 10.1158/1055-9965.EPI-20-0075. View

Chammings F, Ueno Y, Ferre R, Kao E, Jannot A, Chong J . Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy. Radiology. 2017; 286(2):412-420. DOI: 10.1148/radiol.2017170143. View

van Velden F, Kramer G, Frings V, Nissen I, Mulder E, de Langen A . Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation. Mol Imaging Biol. 2016; 18(5):788-95. PMC: 5010602. DOI: 10.1007/s11307-016-0940-2. View

Yoon J, Han K, Lee E, Lee J, Kim E, Moon H . Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma. PLoS One. 2020; 15(2):e0228968. PMC: 7018006. DOI: 10.1371/journal.pone.0228968. View

Liu Z, Zhang X, Shi Y, Wang L, Zhu H, Tang Z . Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017; 23(23):7253-7262. DOI: 10.1158/1078-0432.CCR-17-1038. View

Chen Y, Gao Z, He Y, Mai W, Li J, Zhou M . An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules. Radiology. 2022; 303(3):613-619. DOI: 10.1148/radiol.211455. View

Muller-Franzes G, Nebelung S, Schock J, Haarburger C, Khader F, Pedersoli F . Reliability as a Precondition for Trust-Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction. Diagnostics (Basel). 2022; 12(2). PMC: 8871487. DOI: 10.3390/diagnostics12020247. View

Cheng Z, Huang Y, Huang X, Wu X, Liang C, Liu Z . [Effects of different wavelet filters on correlation and diagnostic performance of radiomics features]. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2019; 44(3):244-250. DOI: 10.11817/j.issn.1672-7347.2019.03.003. View

Heye T, Merkle E, Reiner C, Davenport M, Horvath J, Feuerlein S . Reproducibility of dynamic contrast-enhanced MR imaging. Part II. Comparison of intra- and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. Radiology. 2012; 266(3):812-21. DOI: 10.1148/radiol.12120255. View

10.

Peng S, Liu Y, Lv W, Liu L, Zhou Q, Yang H . Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Health. 2021; 3(4):e250-e259. DOI: 10.1016/S2589-7500(21)00041-8. View

11.

Yoo Y, Ha E, Cho Y, Kim H, Han M, Kang S . Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience. Korean J Radiol. 2018; 19(4):665-672. PMC: 6005935. DOI: 10.3348/kjr.2018.19.4.665. View

12.

Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E . Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep. 2018; 8(1):10545. PMC: 6043486. DOI: 10.1038/s41598-018-28895-9. View

13.

He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z . Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep. 2016; 6:34921. PMC: 5056507. DOI: 10.1038/srep34921. View

14.

Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W . A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer. Clin Cancer Res. 2017; 23(22):6904-6911. DOI: 10.1158/1078-0432.CCR-17-1510. View

15.

Wang Y, Xu F, Agyekum E, Xiang H, Wang Y, Zhang J . Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAF Mutations in Papillary Thyroid Carcinoma. Front Endocrinol (Lausanne). 2022; 13:872153. PMC: 9074386. DOI: 10.3389/fendo.2022.872153. View

16.

Jin Z, Zhu Y, Zhang S, Xie F, Zhang M, Zhang Y . 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. Med Sci Monit. 2020; 26:e918452. PMC: 6977643. DOI: 10.12659/MSM.918452. View

17.

Bagherzadeh-Khiabani F, Ramezankhani A, Azizi F, Hadaegh F, Steyerberg E, Khalili D . A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results. J Clin Epidemiol. 2015; 71:76-85. DOI: 10.1016/j.jclinepi.2015.10.002. View

18.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

19.

Liang J, Huang X, Hu H, Liu Y, Zhou Q, Cao Q . Predicting Malignancy in Thyroid Nodules: Radiomics Score Versus 2017 American College of Radiology Thyroid Imaging, Reporting and Data System. Thyroid. 2018; 28(8):1024-1033. DOI: 10.1089/thy.2017.0525. View

20.

Park V, Han K, Lee E, Kim E, Moon H, Yoon J . Association Between Radiomics Signature and Disease-Free Survival in Conventional Papillary Thyroid Carcinoma. Sci Rep. 2019; 9(1):4501. PMC: 6418281. DOI: 10.1038/s41598-018-37748-4. View