ArticleAnnals of translational medicine2020
Partially cystic thyroid cancer on conventional and elastographic ultrasound: a retrospective study and a machine learning-assisted system.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Malignant Risk Assessment of Cystic-solid Thyroid Nodules Based on Multimodal Ultrasound Features: A Systematic Review and Meta-analysis.Current medical imaging · 2025Pooled it
- Conventional ultrasonography and elastosonography in diagnosis of malignant thyroid nodules: A systematic review and meta-analysis.Frontiers in endocrinology · 2022Pooled it
- Diagnostic Value of Sonographic Features in Distinguishing Malignant Partially Cystic Thyroid Nodules: A Systematic Review and Meta-Analysis.Frontiers in endocrinology · 2021Pooled it
- Artificial intelligence and elastography in diagnostic work-up of thyroid nodules: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Machine learning-based quantification of overall and internal ultrasound characteristics for diagnosing malignant partially cystic thyroid nodules.Frontiers in endocrinology · 2025Article
- Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review).Oncology reports · 2024Review
- Application of thyroglobulin and anti-thyroglobulin antibody combined with emission computed tomography in the adjuvant diagnosis of differentiated thyroid carcinoma.Annals of medicine · 2023Article
- Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.Cancers · 2023Article
- Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto's thyroiditis.Frontiers in oncology · 2022Article
- Analysis of Differential Diagnosis of Benign and Malignant Partially Cystic Thyroid Nodules Based on Ultrasound Characterization With a TIRADS Grade-4a or Higher Nodules.Frontiers in endocrinology · 2022Article
- Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma.Frontiers in medicine · 2021Article
- A Scoring System for Assessing the Risk of Malignant Partially Cystic Thyroid Nodules Based on Ultrasound Features.Frontiers in oncology · 2021Article
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7 authors.
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Abstract
backgroundThyroid carcinoma constitutes the vast majority of all thyroid cancer, most of which is the solid nodule type. No previous studies have examined combining both conventional and elastic sonography to evaluate the diagnostic performance of partially cystic thyroid cancer (PCTC). This retrospective study was designed to evaluate differentiation of PCTC from benign partially cystic nodules with a machine learning-assisted system based on ultrasound (US) and elastography.
methodsPatients with suspicious partially cystic nodules and finally confirmed were included in the study. We performed conventional US and real-time elastography (RTE). The US features of nodules were recorded. The data set was entered into 6 machine-learning algorithms. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated.
resultsA total of 177 nodules were included in this study. Among these nodules, 81 were malignant and 96 were benign. Wreath-shaped feature, micro-calcification, and strain ratio (SR) value were the most important imaging features in differential diagnosis. The random forest classifier was the best diagnostic model.
conclusionsUS features of PCTC exhibited unique characteristics. Wreath-shaped partially cystic nodules, especially with the appearance of micro-calcifications and larger SR value, are more likely to be malignant. The random forest classifier might be useful to diagnose PCTC.
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