ArticleTechnology in cancer research & treatment
Prediction of Cervical Lymph Node Metastasis Using MRI Radiomics Approach in Papillary Thyroid Carcinoma: A Feasibility Study.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.The British journal of radiology · 2024Pooled it
- Pooled it
- Radiomics in thyroid nodule assessment.BMC medical imaging · 2026Review
- MRI characteristics of cervical radioiodine-avid lymph nodes detected on post-therapeutic ¹³¹I whole-body scintigraphy in differentiated thyroid carcinoma.Frontiers in endocrinology · 2026Article
- Diagnostic Performance of Ultrasonography Compared to Cross-Sectional Imaging in Thyroid-Related Nodal Metastasis: A Retrospective Cohort Study from Saudi Arabia.Cancer management and research · 2026Article
- Ultrasound deep learning features for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Frontiers in oncology · 2026Article
- Radiotranscriptomics in papillary thyroid carcinoma complement current noninvasive risk stratification system.Science advances · 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
- High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer.BMC medical imaging · 2025Article
- CT-based texture analysis predicts BRAFFrontiers in oncology · 2025Article
- Exploration of the Evaluation Value of Combined Magnetic Resonance Imaging and Ultrasound Blood Flow Parameters in Cervical Lymph Node Metastasis of Thyroid Cancer.Cancer management and research · 2025Article
- Multimodal MRI Deep Learning for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer.Cancers · 2024Article
- Prediction of cervical lymph node metastasis in solitary papillary thyroid carcinoma based on ultrasound radiomics analysis.Frontiers in oncology · 2024Article
- Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- The progress of radiomics in thyroid nodules.Frontiers in oncology · 2023Review
- Article
- Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study.BMC medical imaging · 2022Article
- Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP.Frontiers in oncology · 2022Article
- Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations.Cancers · 2021Review
- RADIOMICS BASED ON TWO-DIMENSIONAL AND THREE-DIMENSIONAL ULTRASOUND FOR EXTRATHYROIDAL EXTENSION FEATURE PREDICTION IN PAPILLARY THYROID CARCINOMA.Acta endocrinologica (Bucharest, Romania : 2005)Article
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9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC) is critical for treatment and prognosis. To examine the feasibility of MRI radiomics to preoperatively predict cervical LN metastasis in patients with PTC.
methodsBetween January 2015 and March 2018, a total of 61 patients with pathologically confirmed PTC were analyzed retrospectively. The patients were divided into cervical LN metastasis group (n = 37) and no cervical LN metastasis (n = 24). T2WI and T2WI-fat-suppression (T2WI-FS) images were collected. A number of radiomic features were automatically extracted from the largest section of tumor. Three types of classifier (the random forests, the support vector machine classifier and the generalized linear model) based on T2WI and T2WI-FS images of cervical LN metastasis and no cervical LN metastasis were constructed and evaluated with a nested cross-validation scheme.
resultsRadiomic features extracted from T2WI images were more discriminative than T2WI-FS images. The random forests model showed the best discriminate performance with the highest area under the curve (0.85, CI:0.76 -1), accuracy (0.87), sensitivity (0.83), specificity (1.00), positive predictive value (PPV = 1.00) and negative predictive value (NPV = 0.88).
conclusionMRI radiomics analysis based on conventional T2WI and T2WI-FS can predict cervical LN metastasis in patients with PTC, and the radiomics is shown to be an assistant diagnosis tool for radiologists.
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