ArticleBMC cancer2023
Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Radiomics in thyroid nodule assessment.BMC medical imaging · 2026Review
- 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
- From diagnostic marker-to-therapeutic modality: emerging paradigms in tumor biomineralization.Cancer biology & medicine · 2026Review
- Can GPT-5.0 Interpret Thyroid Ultrasound Images? A Comparative TI-RADS Analysis with an Expert Radiologist.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based classification of thyroid nodules using uncertainty-aware multi-modal ultrasound imaging.Scientific reports · 2026Article
- Diagnostic performance of multimodal ultrasound-based deep learning models in differentiating benign and malignant thyroid nodules.Frontiers in oncology · 2026Article
- A "calcification"-enhanced deep learning approach for precise differentiation of thyroid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Prediction of thyroid function based on CT radiomics: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- Malignancy prediction for calcified thyroid nodules using deep learning based on ultrasound dynamic videos.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A multicenter diagnostic study of thyroid nodule with Hashimoto's thyroiditis enabled by Hashimoto's thyroiditis nodule-artificial intelligence model.European radiology · 2025Article
- Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.Endocrine · 2025Article
- Predictive Factors for Malignancy in Atypiai of Undetermined Significance (AUS) Thyroid Nodules: A Comprehensive Retrospective Analysis.Current oncology (Toronto, Ont.) · 2025Article
- Clinical Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- Automated Assessment of the Pulmonary Artery-to-Ascending Aorta Ratio in Fetal Cardiac Ultrasound Screening Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2024Article
- Review
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15 authors.
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Abstract
backgroundCalcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules.
methodsThis retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists.
resultsThe Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model's parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data.
conclusionsThis study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL.
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