ArticleFrontiers in endocrinology2020
Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.
Article in Frontiers in endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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52 citing papers in PubMed, 3 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- 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
- Pooled it
- Development and validation of machine learning models for predicting synchronous lung metastasis in United States colorectal cancer patients: a SEER database analysis.Translational cancer research · 2026Article
- An interpretable machine learning model for diagnostic classification of liver cancer using multivariable clinical data.American journal of cancer research · 2026Article
- Development and validation of a machine learning model for predicting coronary artery disease risk: a retrospective study.Frontiers in cardiovascular medicine · 2026Article
- Development and validation of a machine learning-based diagnostic model for obstructive coronary artery disease in hypertensive patients using composite inflammatory and lipid markers.Frontiers in cardiovascular medicine · 2026Article
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- Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.BMJ open · 2025Article
- Machine Learning for Thyroid Cancer Detection, Presence of Metastasis, and Recurrence Predictions-A Scoping Review.Cancers · 2025Review
- Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models.Frontiers in endocrinology · 2025Article
- Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.Scientific reports · 2024Article
- Article
- Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma.Diagnostics (Basel, Switzerland) · 2024Review
- A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.Radiation oncology (London, England) · 2024Article
- Article
- Analysis of risk factors for papillary thyroid carcinoma and the association with thyroid function indicators.Frontiers in endocrinology · 2024Article
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- A nomogram based on ultrasound radiomics for predicting the invasiveness of cN0 single papillary thyroid microcarcinoma.Gland surgery · 2023Article
- Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic central lymph node dissection is still controversial. There are no reliable models for predicting CLNM. This study aimed to develop predictive models for CLNM by machine learning (ML) algorithms. Methods: Patients with PTC who underwent initial thyroid resection at our hospital between January 2018 and December 2019 were enrolled. A total of 22 variables, including clinical characteristics and ultrasonography (US) features, were used for conventional univariate and multivariate analysis and to construct ML-based models. A 5-fold cross validation strategy was used for validation and a feature selection approach was applied to identify risk factors. Results: The areas under the receiver operating characteristic curve (AUC) of 7 models ranged from 0.680 to 0.731. All models performed significantly better than US (AUC=0.623) in predicting CLNM (P<0.05). In decision curve, most of the models also performed better than US. The gradient boosting decision tree model with 7 variables was identified as the best model because of its best performance in both ROC (AUC=0.731) and decision curves. Based on multivariate analysis and feature selection, young age, male sex, low serum thyroid peroxidase antibody and US features such as suspected lymph nodes, microcalcification and tumor size > 1.1 cm were the most contributing predictors for CLNM. Conclusions: It is feasible to develop predictive models of CLNM in PTC patients by incorporating clinical characteristics and US features. The ML algorithm may be a useful tool for the prediction of lymph node metastasis in thyroid cancer.
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