ArticleFrontiers in oncology2021
Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis.Frontiers in endocrinology · 2023Pooled it
- Machine Learning for diagnosis of malignant thyroid nodules based on thyroid ultrasound: Systematic review and meta-analysis of studies with external datasets.European journal of radiology open · 2026Article
- High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging.Joint diseases and related surgery · 2026Article
- Syncretic Grad-CAM Integrated ViT-CNN Hybrids with Inherent Explainability for Early Thyroid Cancer Diagnosis from Ultrasound.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models.Scientific reports · 2025Article
- Application of artificial intelligence in the ultrasonographic diagnosis of thyroid nodules.Journal of ultrasonography · 2025Article
- Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules.Frontiers in endocrinology · 2025Article
- A predictive nomogram of thyroid nodules based on deep learning ultrasound image analysis.Frontiers in endocrinology · 2025Article
- Ultrasound radiomics and genomics improve the diagnosis of cytologically indeterminate thyroid nodules.Frontiers in endocrinology · 2025Review
- Multimodal MRI Deep Learning for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer.Cancers · 2024Article
- Artificial intelligence assisted diagnosis of early tc markers and its application.Discover oncology · 2024Review
- The Utility of Contrast-Enhanced Ultrasound (CEUS) in Assessing the Risk of Malignancy in Thyroid Nodules.Cancers · 2024Article
- Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.European radiology · 2024Article
- Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Development of a machine learning-based fine-grained risk stratification system for thyroid nodules using predefined clinicoradiological features.European radiology · 2023Article
- A narrative review on the application of artificial intelligence in renal ultrasound.Frontiers in oncology · 2023Review
Corrections and comments
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10 authors.
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Abstract
objectiveThe purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS, TR) from the American College of Radiology (ACR). DESIGN AND
methodsFrom June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms.
resultsIn the independent test set, the DL algorithm of best performance got an AUC of 0.904, 0.845, 0.829 in TR4, TR5, and TR4&5, respectively. The sensitivity and specificity of the optimal model was 0.829, 0.831 on TR4, 0.846, 0.778 on TR5, 0.790, 0.779 on TR4&5, versus the radiologists of 0.686 (
conclusionsThe study demonstrated that DL could improve the differentiation of malignant from benign thyroid nodules and had significant potential for clinical application on TR4 and TR5.
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