ReviewCancers2021
Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Association of radiomic features with genomic signatures in thyroid cancer: a systematic review.Journal of translational medicine · 2024Pooled it
- Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.Reviews in endocrine & metabolic disorders · 2024Pooled it
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Development and validation of a predictive model for thyroid nodule malignancy risk based on intralesional and perilesional ultrasound radiomic features.Gland surgery · 2026Article
- Development and validation of an Automated computed tomography segmentation model for thyroid nodules using a channel attention high-resolution network.Gland surgery · 2026Article
- Application of transformer attention mechanism-based multimodal deep learning model in the diagnosis of papillary thyroid carcinoma.BMC medical imaging · 2026Article
- Radiomics in thyroid nodule assessment.BMC medical imaging · 2026Review
- Integrative Bioinformatics Analysis Reveals COL13A1 and COL23A1 as Potential Diagnostic and Prognostic Biomarkers in Thyroid Cancer.Health science reports · 2026Article
- A multimodal study on predicting extrathyroidal extension of papillary thyroid carcinoma based on radiopathomics.BMC endocrine disorders · 2026Article
- Article
- Predictive Factors for Distant Metastasis in Follicular Thyroid Carcinoma: Analysis of Clinical, Genetic, and Radiomics Features.Journal of the Korean Society of Radiology · 2026Article
- The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.Current medical imaging · 2026Article
- Cervical lymph node response to 131I therapy in differentiated thyroid cancer using radiomics and clinical features.Frontiers in oncology · 2026Article
- Global epidemiology of thyroid cancer: trends in incidence, mortality, and DALYs from 1990 to 2021.Open medicine (Warsaw, Poland) · 2026Article
- Revolutionizing thyroid nodule diagnosis in Hashimoto's thyroiditis: AI-driven radiomics and deep learning model.International journal of surgery (London, England) · 2025Article
- Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy.Clinical nuclear medicine · 2025Article
- Correlation of multiple peripheral blood parameters with metastasis and invasion of papillary thyroid cancer: a retrospective cohort study.Endocrine · 2025Article
- A risk stratification model based on ultrasound radiologic features for cervical metastatic lymph nodes in papillary thyroid cancer.World journal of surgical oncology · 2025Article
- Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics.Frontiers in oncology · 2025Article
- Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.Gland surgery · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.
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Registered trials
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.