ArticleTechnology in cancer research & treatment2019
Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images.
Article in Technology in cancer research & treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of machine learning and deep learning algorithms for thyroid cancer metastasis: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.The British journal of radiology · 2024Pooled it
- Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study.BMC medicine · 2024Trial
- Article
- Prediction of malignancy and metastasis of thyroid cancer by combined feature sets through advanced machine learning.BMC medical informatics and decision making · 2026Article
- Article
- Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid carcinoma.PloS one · 2026Article
- Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a study of ultrasound and clinical features.Frontiers in endocrinology · 2026Article
- Prediction of lymph node metastasis in cN0 papillary thyroid carcinoma using a random forest-based radiomics model integrating contrast-enhanced ultrasound and clinical data.Frontiers in oncology · 2026Article
- Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.International journal of surgery (London, England) · 2025Article
- Development and validation of a novel echocardiography-based nomogram for the streamlined classification of cardiac tumors in cancer patients.Quantitative imaging in medicine and surgery · 2025Article
- The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.Ultrasonic imaging · 2025Article
- Integrating Deep Learning and Radiomics in Differentiating Papillary Thyroid Microcarcinoma from Papillary Thyroid Carcinoma with Ultrasound Images.Cancer management and research · 2025Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Radiomics and deep learning for large volume lymph node metastasis in papillary thyroid carcinoma.Gland surgery · 2024Article
- Prediction model for lateral lymph node metastasis of papillary thyroid carcinoma in children and adolescents based on ultrasound imaging and clinical features: a retrospective study.BMC medical imaging · 2024Article
- Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma.Diagnostics (Basel, Switzerland) · 2024Review
- Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.BMC cancer · 2024Article
- Targeted sequencing of DNA/RNA combined with radiomics predicts lymph node metastasis of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A CT based radiomics analysis to predict the CN0 status of thyroid papillary carcinoma: a two- center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
5 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
backgroundPapillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images.
methodsFour hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model.
resultsAmong 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710.
conclusionPapillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.
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