ArticleThe Journal of clinical endocrinology and metabolism2025
Machine Learning: A Multicenter Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.
Article in The Journal of clinical endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Machine learning: predicting lymph node metastasis around the entrance point to the recurrent laryngeal nerve in cN0 papillary thyroid carcinoma.Frontiers in endocrinology · 2026Article
- Lymph node metastasis in patients with hepatocellular carcinoma using machine learning: a population-based study.Frontiers in oncology · 2025Article
- Machine learning and Shapley Additive exPlanations to predict metastasis of lymph nodes posterior to the recurrent laryngeal nerve in cN0 papillary thyroid carcinoma.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundThe necessity of prophylactic lateral neck dissection for cN0 papillary thyroid carcinoma (PTC) remains debated. This study aimed to compare traditional nomograms with machine learning (ML) models for predicting ipsilateral lateral and level II, III, and IV lymph node metastasis (LNM).
methodsData from 1616 PTC patients diagnosed via fine-needle aspiration biopsy from hospital A were split into training and testing sets (7:3). Two hundred forty-three patients from hospital B served as a validation set. Four dependent variables-ipsilateral lateral and level II, III, and IV LNM-were analyzed. Eight ML models [logistic regression, decision tree, random forest (RF), gradient boosting, support vector machine, K-nearest neighbor, Gaussian naive Bayes, neural networks] were developed and validated using 10-fold cross-validation and grid search hyperparameter tuning. Models were assessed using 11 metrics including accuracy, area under the curve (AUC), specificity, and sensitivity. The best was compared with nomograms using the probability-based ranking model approach (PMRA).
resultsRF outperformed other approaches achieving accuracy, AUC, specificity, and sensitivity of 0.773/0.728, 0.858/0.799, 0.984/0.935, 0.757/0.807 in the testing/validation sets, respectively, for ipsilateral LLNM. A streamlined model based on the top 10 contributing features that includes ipsilateral central lymph node metastasis rate, extrathyroidal extension, and ipsilateral central lymph node metastasis number retained strong performance and clearly surpassed a traditional nomogram approach based on multiple metrics and PMRA analysis. Similar results were obtained for the other dependent variables, with the RF models relying on distinct but overlapping sets of features. Clinical tool implementation is facilitated via a web-based calculator for each of the 4 dependent variables.
conclusionML, especially RF, reliably predicts lateral LNM in cN0 PTC patients, outperforming traditional nomograms.
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