ArticleFrontiers in endocrinology2022
A comparative analysis of eight machine learning models for the prediction of lateral lymph node metastasis in patients with papillary thyroid carcinoma.
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 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 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
- Prediction of lymph node metastasis in cN0 papillary thyroid carcinoma using a random forest-based radiomics model integrating contrast-enhanced ultrasound and clinical data.Frontiers in oncology · 2026Article
- Machine Learning: A Multicenter Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.The Journal of clinical endocrinology and metabolism · 2025Article
- Enhancing prediction of cervical lymph node metastasis in papillary thyroid carcinoma through nodule-oriented quantification: combined S-Detect and ultrasound elastography.Quantitative imaging in medicine and surgery · 2025Article
- Multimodal prognostic models for bladder urothelial carcinoma: uroplakin III combined with serum and demographic data.Frontiers in oncology · 2025Article
- Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.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
- Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.World journal of surgical oncology · 2024Article
- Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.Frontiers in endocrinology · 2024Article
- 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
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7 authors.
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Abstract
Background: Lateral lymph node metastasis (LLNM) is a contributor for poor prognosis in papillary thyroid cancer (PTC). We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of LLNM in these patients. Methods: This is retrospective study comprising 1236 patients who underwent initial thyroid resection at our institution between January 2019 and March 2022. All patients were randomly split into the training dataset (70%) and the validation dataset (30%). Eight ML algorithms, including the Logistic Regression, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of LLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis. Results: Among the eight ML algorithms, RF had the highest AUC (0.975), with sensitivity and specificity of 0.903 and 0.959, respectively. It was therefore used to develop as prediction model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: central lymph node ratio, size, central lymph node metastasis, number of foci, location, body mass index, aspect ratio, sex and extrathyroidal extension. Conclusion: By combining clinical and sonographic characteristics, ML algorithms can achieve acceptable prediction of LLNM, of which the RF model performs best. ML algorithms can help clinicians to identify the risk probability of LLNM in PTC patients.
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