ArticleHeliyon2024
A model based on ultrasound and clinical factors to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- An ultrasonographic tumor location-based model for predicting central lymph node metastasis in unifocal papillary thyroid microcarcinoma.Frontiers in endocrinology · 2026Observational
- An interpretable machine learning model for predicting central lymph node metastasis in cN0 T1-T2 papillary thyroid carcinoma: a retrospective study.Frontiers in endocrinology · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The prevalence of thyroid malignancies has sharply elevated in the past few years, and a large number of newly diagnosed thyroid malignancies was papillary thyroid microcarcinomas (PTMC). The efficacy of prophylactic central lymph node dissection (PCLND) in patients with clinical lymph node-negative (cN0) PTMC is still debatable. In this study, we aimed to create a predictive model to assess the likelihood of central lymph node metastasis (CLNM) in cN0 PTMC. Methods: Two hundred and fifty three patients diagnosed with cN0 PTMC who received surgery in the First People's Hospital of Kunshan from October 2018 to June 2023 were enrolled. Multivariate logistic regression was employed to evaluate the patient's clinical and ultrasonographic information to determine independent factors. Two prediction models were generated and their ability to evaluate the likelihood of CLNM in cN0 PTMC was determined and compared. Results: Multivariate analysis based on clinical characteristics revealed that, CLNM was markedly linked to age, tumor size, and extrathyroidal infiltration in cN0 PTMC. Multivariate analysis utilizing clinical and ultrasound features demonstrated that age, tumor size, extrathyroidal infiltration, shape, microcalcification were independent risk factors for CLNM. The analysis of the receiver operating characteristic curve demonstrated that the predictive nomogram utilizing clinical and ultrasound features was more beneficial for predicting CLNM. And decision curve demonstrates the same. The model's calibration curve exhibited strong consistency. Conclusions: Age, tumor size, extrathyroidal infiltration, shape, microcalcification are significant independent factors of CLNM in cN0 PTMC. A predictive model derived from these independent clinical and ultrasound factors has a good value, but further validation is still required.
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Registered trials
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