ArticleFrontiers in oncology2022
Nomogram for preoperative estimation risk of cervical lymph node metastasis in medullary thyroid carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Ultrasound-based radiomics for prediction of lateral neck lymph node metastasis in patients with medullary thyroid cancer.Gland surgery · 2026Article
- Development and validation of a novel nomogram for predicting lateral lymph node metastasis in medullary thyroid carcinoma.Frontiers in endocrinology · 2025Article
- Review
- The machine learning-based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.Cancer medicine · 2024Article
- A practical nomogram for preoperatively predicting lateral cervical lymph node metastasis in medullary thyroid carcinoma: a dual-center retrospective study.Frontiers in endocrinology · 2024Article
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Authors and funding
6 authors.
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Abstract
Objectives: Cervical lymph node metastasis (CLNM) is common in medullary thyroid carcinoma (MTC), but how to manage cervical lymph node involvement of clinically negative MTC is still controversial. This study evaluated the preoperative features and developed an ultrasound (US)-based nomogram to preoperatively predict the CLNM of MTC. Materials and methods: A total of 74 patients with histologically confirmed MTC were included in this retrospective study and assigned to the CLNM-positive group and CLNM-negative group based on the pathology. The associations between CLNM and preoperative clinical and sonographic characteristics (size, location, solid component, shape, margin, echogenicity, calcification, and extracapsular invasion of the tumor) were evaluated by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the risk of the CLNM of MTC was built and assessed in terms of discrimination, calibration, and clinical usefulness. Results: The nomogram was based on three factors (tumor margin, US-reported suspicious lymph node, and extracapsular invasion US features) and exhibited good discrimination with an area under the curve (AUC) of 0.919 (95% CI, 0.856-0.932). The calibration curves of the nomogram displayed a good agreement between the probability as predicted by the nomogram and the actual CLNM incidence. Conclusions: We constructed and validated a US-based nomogram to predict the risk of CLNM in MTC patients, which can be easily evaluated before surgery. This model is helpful for clinical decision-making.
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