ArticleEClinicalMedicine2023
Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter study.
Article in EClinicalMedicine, 2023. 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.
- Diagnostic performance and generalizability of a clinical-ultrasound radiomics model for predicting extrathyroidal extension in thyroid carcinoma: a retrospective study.Annals of medicine · 2026Article
- Preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma: a nomogram integrating dual-plane ultrasound-based radiomics and clinical features.Gland surgery · 2026Article
- Article
- A multimodal study on predicting extrathyroidal extension of papillary thyroid carcinoma based on radiopathomics.BMC endocrine disorders · 2026Article
- Combined clinicopathological and ultrasonographic features for predicting extrathyroidal extension in thyroid cancer.Quantitative imaging in medicine and surgery · 2026Article
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- Adaptive Bandelet Transform and Transfer Learning for Geometry-Aware Thyroid Cancer Ultrasound Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Construction and validation of a predictive model for the acceptance of kidney xenotransplantation among patients awaiting kidney transplantation: a cross-sectional study from China.BMC medical ethics · 2025Article
- Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.Endocrine · 2025Article
- Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer.Biomedical engineering online · 2025Article
- Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Endocrine · 2025Article
- Quantitative analysis of studies that use artificial intelligence on thyroid cancer: a 20-year bibliometric analysis.Frontiers in oncology · 2025Review
- A predictive nomogram of thyroid nodules based on deep learning ultrasound image analysis.Frontiers in endocrinology · 2025Article
- Construction and validation of a predictive model for exclusive breastfeeding at discharge based on the information-motivation-behavioral skills theory.Frontiers in medicine · 2025Article
- Prediction of TNFRSF9 expression and molecular pathological features in thyroid cancer using machine learning to construct Pathomics models.Endocrine · 2024Article
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study.Frontiers in endocrinology · 2023Article
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15 authors.
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Abstract
Background: The presence of gross extrathyroidal extension (ETE) in thyroid cancer will affect the prognosis of patients, but imaging examination cannot provide a reliable diagnosis for it. This study was conducted to develop a deep learning (DL) model for localization and evaluation of thyroid cancer nodules in ultrasound images before surgery for the presence of gross ETE. Methods: From January 2016 to December 2021 grayscale ultrasound images of 806 thyroid cancer nodules (4451 images) from 4 medical centers were retrospectively analyzed, including 517 no gross ETE nodules and 289 gross ETE nodules. 283 no gross ETE nodules and 158 gross ETE nodules were randomly selected from the internal dataset to form a training set and validation set (2914 images), and a multitask DL model was constructed for diagnosing gross ETE. In addition, the clinical model and the clinical and DL combined model were constructed. In the internal test set [974 images (139 no gross ETE nodules and 83 gross ETE nodules)] and the external test set [563 images (95 no gross ETE nodules and 48 gross ETE nodules)], the diagnostic performance of DL model was verified based on the pathological results. And then, compared the results with the diagnosis by 2 senior and 2 junior radiologists. Findings: In the internal test set, DL model demonstrated the highest AUC (0.91; 95% CI: 0.87, 0.96), which was significantly higher than that of two senior radiologists [(AUC, 0.78; 95% CI: 0.71, 0.85; Interpretation: The DL model based on ultrasound imaging is a simple and helpful tool for preoperative diagnosis of gross ETE thyroid cancer, and its diagnostic performance is equivalent to or even better than that of senior radiologists. Funding: Jiangxi Provincial Natural Science Foundation (20224BAB216079), the Key Research and Development Program of Jiangxi Province (20181BBG70031), and the Interdisciplinary Innovation Fund of Natural Science, Nanchang University (9167-28220007-YB2110).
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