ArticleFrontiers in endocrinology2022
Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 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
- Nomogram Model Based on Iodine Nutrition and Clinical Characteristics of Papillary Thyroid Carcinoma to Predict Lateral Lymph Node Metastasis.Cancer control : journal of the Moffitt Cancer CenterTrial
- [Efficacy analysis of gasless robotic surgery via transaxillary approach for unilateral N1b PTC].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Article
- Diagnostic Value of [Molecular imaging and biology · 2025Article
- Article
- Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.Data in brief · 2025Article
- Article
- Machine Learning for Thyroid Cancer Detection, Presence of Metastasis, and Recurrence Predictions-A Scoping Review.Cancers · 2025Review
- Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.Frontiers in endocrinology · 2025Article
- Risk factors for predicting lateral lymph node metastasis of papillary thyroid carcinoma based on LASSO-logistic regression.Frontiers in endocrinology · 2025Article
- Prediction of lateral lymph node metastasis with short diameter less than 8 mm in papillary thyroid carcinoma based on radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.Scientific reports · 2024Article
- Clinical prediction models for cervical lymph node metastasis of papillary thyroid carcinoma.Endocrine · 2024Article
- The diagnosis and management of small and indeterminate lymph nodes in papillary thyroid cancer: preoperatively and intraoperatively.Frontiers in endocrinology · 2024Review
- Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach.Journal of clinical medicine · 2023Article
- Cervical lymph node metastasis prediction of postoperative papillary thyroid carcinoma beforeFrontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients. Methods: Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians. Results: A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/. Conclusion: The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.
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