ArticleFrontiers in oncology2022
Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 27 citations in OpenAlex.
- RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.Scientific reports · 2026Article
- Deep Learning Based on B-Mode and Color Doppler Ultrasound for Differentiation of Primary Thyroid Lymphoma and Hashimoto's Thyroiditis: A Retrospective Single-Center Study.Diagnostics (Basel, Switzerland) · 2026Article
- The Role of Cytology, Histology and Molecular Pathology in the Diagnostic Process of Thyroid Nodules.Cancers · 2026Review
- Diagnostic comparison of TI-RADS and a nomogram for thyroid nodules in Northwestern China.Discover oncology · 2026Article
- Artificial intelligence and elastography in diagnostic work-up of thyroid nodules: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 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
- Improving Non-Invasive Prediction of Thyroid Nodule Malignancy: A Machine Learning-Based Clinical Approach.Cancer management and research · 2026Article
- A Thyroid Nodule Ultrasound Image Grading Model Integrating Medical Prior Knowledge.Journal of imaging informatics in medicine · 2025Article
- Combining curriculum learning and weakly supervised attention for enhanced thyroid nodule assessment in ultrasound imaging.Quantitative imaging in medicine and surgery · 2025Article
- Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.Endocrine · 2025Article
- Diagnostic Nomogram Model for ACR TI-RADS 4 Nodules Based on Clinical, Biochemical Data and Sonographic Patterns.Clinical endocrinology · 2025Article
- The value of a combined model based on ultra-radiomics and multi-modal ultrasound in the benign-malignant differentiation of C-TIRADS 4A thyroid nodules: a prospective multicenter study.Frontiers in oncology · 2025Article
- Investigating the Applicability of the McGill Thyroid Nodule Score (MTNS) in Patients Undergoing Surgery for Thyroid Nodules: A Comparison Between Patients with and Without Papillary Thyroid Cancer.Journal of clinical practice and research · 2024Article
- A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients.Frontiers in oncology · 2024Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to differentially diagnose thyroid nodules (TNs) of Thyroid Imaging Reporting and Data System (TI-RADS) 3-5 categories using a deep learning (DL) model based on multimodal ultrasound (US) images and explore its auxiliary role for radiologists with varying degrees of experience. Methods: Preoperative multimodal US images of 1,138 TNs of TI-RADS 3-5 categories were randomly divided into a training set (n = 728), a validation set (n = 182), and a test set (n = 228) in a 4:1:1.25 ratio. Grayscale US (GSU), color Doppler flow imaging (CDFI), strain elastography (SE), and region of interest mask (Mask) images were acquired in both transverse and longitudinal sections, all of which were confirmed by pathology. In this study, fivefold cross-validation was used to evaluate the performance of the proposed DL model. The diagnostic performance of the mature DL model and radiologists in the test set was compared, and whether DL could assist radiologists in improving diagnostic performance was verified. Specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristics curves (AUC) were obtained. Results: The AUCs of DL in the differentiation of TNs were 0.858 based on (GSU + SE), 0.909 based on (GSU + CDFI), 0.906 based on (GSU + CDFI + SE), and 0.881 based (GSU + Mask), which were superior to that of 0.825-based single GSU ( Conclusion: The DL models based on multimodal US images showed exceptional performance in the differential diagnosis of suspicious TNs, effectively increased the diagnostic efficacy of TN evaluations by junior radiologists, and provided an objective assessment for the clinical and surgical management phases that follow.
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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.