ArticleCancers2022
Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ultrasound-based artificial intelligence for predicting cervical lymph node metastasis in papillary thyroid cancer: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Development and internal validation of a multivariable MRI-based radiomics prediction model for preoperative cervical lymph node metastasis in thyroid cancer.Gland surgery · 2026Article
- Development of a clinically applicable prediction tool for lymph node metastasis in papillary thyroid carcinoma using lasso-logistic regression.BMC cancer · 2026Article
- Ultrasound deep learning features for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Frontiers in oncology · 2026Article
- Noninvasive immune-inflammatory profiling by ultrasound radiomics for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma.Frontiers in immunology · 2026Article
- Development and validation of a preoperative nomogram for predicting contralateral occult carcinoma and central lymph node metastasis in T1b-T2 stage papillary thyroid carcinoma.Frontiers in endocrinology · 2026Article
- Machine Learning: A Multicenter Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.The Journal of clinical endocrinology and metabolism · 2025Article
- Diagnostic value of preoperative advanced doppler imaging with cervical maneuvers in the detection of central cervical lymph node metastasis in papillary thyroid carcinoma.BMC medical imaging · 2025Article
- Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.BMC medical informatics and decision making · 2025Article
- Development and validation of a CT-based radiomics nomogram for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Cancer biomarkers : section A of Disease markers · 2025Article
- Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms.European journal of medical research · 2025Article
- A novel deep learning model based on multimodal contrast-enhanced ultrasound dynamic video for predicting occult lymph node metastasis in papillary thyroid carcinoma.Frontiers in endocrinology · 2025Article
- Prediction of lateral lymph node metastasis with short diameter less than 8 mm in papillary thyroid carcinoma based on radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma Using a Clinical-Radiomics Nomogram Based on B-Mode and Contrast-Enhanced Ultrasound.Diagnostics (Basel, Switzerland) · 2023Article
- Article
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Authors and funding
12 authors.
Funding
Abstract
We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (
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