ReviewMedical science monitor : international medical journal of experimental and clinical research2022
A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease.
Review in Medical science monitor : international medical journal of experimental and clinical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Multiparametric Abdominal Ultrasound as a Source of Imaging Biomarkers in Predictive and Personalized Medicine: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development and Validation of Machine-Learning-Based Prediction Models for Thyroid Diseases During Pregnancy.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Predictive models for contralateral occult thyroid carcinoma and central lymph node metastasis in unilateral multifocal papillary thyroid carcinoma based on ultrasound.Quantitative imaging in medicine and surgery · 2026Article
- Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.BMJ open · 2025Article
- Clinical-radiomics combination model for predicting the short-term efficacy of bipolar transurethral enucleation of the prostate in patients with benign prostatic hyperplasia.Current urology · 2025Article
- Construction of predictive models for contralateral occult thyroid carcinoma and central lymph node metastasis in unilateral papillary thyroid carcinoma using machine learning.Frontiers in oncology · 2025Article
- Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.Gland surgery · 2024Article
- A Machine Learning Model Based on Thyroid US Radiomics to Discriminate Between Benign and Malignant Nodules.Cancers · 2024Article
- Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer.BMC medical imaging · 2024Article
- CT-based radiomics features for the differential diagnosis of nodular goiter and papillary thyroid carcinoma: an analysis employing propensity score matching.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.