ArticleCancer medicine2022
Prediction of lung metastases in thyroid cancer using machine learning based on SEER database.
Article in Cancer medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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47 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
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- Comprehensive Analysis of Cuproptosis-Related Genes According to Cancer Stage and Their Prognostic Value in Cervical Cancer.International journal of molecular sciences · 2026Article
- Machine learning approaches for predicting differentiated thyroid cancer recurrence using thyroglobulin levels and whole-body scans: a retrospective cohort study.Scientific reports · 2026Article
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- The relationship between urinary tract infections and sepsis: results from Mendelian randomization and MIMIC data study.World journal of urology · 2026Article
- Complete resection of solitary pulmonary metastases in thyroid cancer: a case series report.Journal of cardiothoracic surgery · 2026Article
- The value of machine learning models in predicting lung metastasis in papillary thyroid carcinoma.Frontiers in endocrinology · 2026Article
- Patterns of radioiodine uptake combined with pulmonary nodule size in predicting long-term clinical outcomes of differentiated thyroid carcinoma patients with lung metastasis alone.Asia Oceania journal of nuclear medicine & biology · 2026Article
- Development and validation of a prognostic prediction model for gastric cancer based on lipophagy-related genes.Translational cancer research · 2025Article
- Cost-effectiveness of risk model-based lung cancer screening in smokers and nonsmokers in China.BMC medicine · 2025Article
- Explainable machine learning for predicting lung metastasis of colorectal cancer.Scientific reports · 2025Article
- Machine Learning for Thyroid Cancer Detection, Presence of Metastasis, and Recurrence Predictions-A Scoping Review.Cancers · 2025Review
- Combining pelvic floor ultrasonography with deep learning to diagnose anterior compartment organ prolapse.Quantitative imaging in medicine and surgery · 2025Article
- Liver metastases in thyroid cancer: epidemiology, risk stratification and survival outcomes in the immunotherapy era.Frontiers in immunology · 2025Article
- A machine learning model for predicting bone and/or lung metastasis in differentiated thyroid carcinoma: enhancing precision in risk stratification.Frontiers in endocrinology · 2025Article
- Explainable multi-modal machine learning for predicting occult pulmonary metastases in differentiated thyroid cancer: a SHAP-based approach prior to radioactive iodine scans.Frontiers in medical technology · 2025Article
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- Machine learning-based identification of high-risk bone metastasis factors after radical prostatectomy in prostate cancer.Frontiers in oncology · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLung metastasis (LM) is one of the most frequent distant metastases of thyroid cancer (TC). This study aimed to develop a machine learning algorithm model to predict lung metastasis of thyroid cancer for providing relative information in clinical decision-making.
methodsData comprising of demographic and clinicopathological characteristics of patients with thyroid cancer were extracted from the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, which is employed to develop six machine learning algorithm models support vector machine (SVM), logistic regression (LR), eXtreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). Compared and evaluated models by the following indicators: accuracy, precision, recall rate, F1-score, the area under the ROC curve (AUC) value and Brier score, and interpreted the association between clinicopathological characteristics and target variables based on the best model.
resultsNine thousand nine hundred and fifty patients were selected, which including 212 patients (2.1%) with lung metastasis, and 9738 patients without lung metastasis (97.9%). Multivariate logistic regression showed that age, T stage, N stage, and histological type were independent factors in TC with LM. Evaluation indicators of the best model- RF were as following: accuracy (0.99), recall rate (0.88), precision (0.61), F1-score (0.72), AUC value (0.99), and the Brier score (0.016).
conclusionRF learning model performed better and can be applied to forecast lung metastasis of thyroid cancer, and offer valuable and significant reference for clinicians' decision-making in advance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.