ArticleJournal of clinical medicine2020
Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.The British journal of radiology · 2024Pooled it
- Ultrasound radiomics features for identifying the masseter muscle in patients with masticatory muscle tendon-aponeurosis hyperplasia: A pilot study.Imaging science in dentistry · 2026Article
- Predictive Factors for Distant Metastasis in Follicular Thyroid Carcinoma: Analysis of Clinical, Genetic, and Radiomics Features.Journal of the Korean Society of Radiology · 2026Article
- Molecular Markers in Follicular Thyroid Carcinoma: Moving Toward Better Prediction of Distant Metastasis.Journal of the Korean Society of Radiology · 2026Article
- Article
- The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.Current medical imaging · 2026Article
- Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data.Scientific reports · 2025Article
- A dual-center study: can ultrasound radiomics differentiate type I and type II epithelial ovarian cancer patients with normal CA125 levels?The British journal of radiology · 2024Article
- The superior value of radiomics to sonographic assessment for ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma: a retrospective study.Radiology and oncology · 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
- Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer.BMC medical imaging · 2024Article
- Prognosis and influencing factors of follicular thyroid cancer.Cancer medicine · 2024Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer.Journal of medical ultrasonics (2001) · 2023Article
- Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer.The British journal of radiology · 2022Article
- A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Review
- Value of Whole-Thyroid CT-Based Radiomics in Predicting Benign and Malignant Thyroid Nodules.Frontiers in oncology · 2022Article
- Nomogram individually predicts the risk for distant metastasis and prognosis value in female differentiated thyroid cancer patients: A SEER-based study.Frontiers in oncology · 2022Article
- Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer.Annals of translational medicine · 2021Article
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Authors and funding
6 authors.
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Abstract
We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis (
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