ArticleUltrasonography (Seoul, Korea)2020
Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland.
Article in Ultrasonography (Seoul, Korea), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022Pooled it
- Intelligent Diagnosis of Follicular Carcinoma Thyroid Cancer with a Novel Deep Learning Model.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based ultrasound diagnostic model for follicular thyroid carcinoma.European radiology · 2026Article
- Multimodal model enhances qualitative diagnosis of hypervascular thyroid nodules: integrating radiomics and deep learning features based on B-mode and PDI images.Gland surgery · 2025Article
- Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.JMIR cancer · 2025Article
- Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.Scientific reports · 2025Article
- Multimodal ultrasound radiomics model combined with clinical model for differentiating follicular thyroid adenoma from carcinoma.BMC medical imaging · 2025Article
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI.Journal of imaging informatics in medicine · 2024Article
- Intratumoral and peritumoral radiomics based on ultrasound for the differentiation of follicular thyroid neoplasm.Gland surgery · 2024Article
- Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.Quantitative imaging in medicine and surgery · 2024Article
- The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms.BMC medical imaging · 2024Article
- Artificial intelligence in thyroid ultrasound.Frontiers in oncology · 2023Review
- The progress of radiomics in thyroid nodules.Frontiers in oncology · 2023Review
- Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules.Frontiers in endocrinology · 2023Article
- Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules.Frontiers in endocrinology · 2023Article
- Article
- Differentiate Thyroid Follicular Adenoma from Carcinoma with Combined Ultrasound Radiomics Features and Clinical Ultrasound Features.Journal of digital imaging · 2022Article
- Clinicoradiological Characteristics in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid: A Multicenter Cohort Study.Korean journal of radiology · 2022Article
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Authors and funding
10 authors.
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Abstract
purposeThis study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US).
methodsIn this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared.
resultsIn total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement.
conclusionMachine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.
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