[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126334-en":3,"doc-seo-126334-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126334,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A machine learning model for predicting bone and/or lung metastasis in differentiated thyroid carcinoma - enhancing precision in risk stratification","Differentiated thyroid cancer has an overall favorable prognosis, but a subset develops aggressive disease with distant metastases, especially to bone and lung, which markedly reduces survival. Existing prediction approaches often depend on limited clinical variables and show suboptimal accuracy and generalizability. This study develops a machine learning model using SEER data and external validation to improve risk stratification and enable earlier intervention in high-risk patients.","TYPE Original Research PUBLISHED 08 September 2025 DOI 10.3389/fendo.2025.1528392  \nOPEN ACCESS  \nEDITED BY  \nNatarajan Bhaskaran,  \nSaveetha Medical College & Hospital, India  \nREVIEWED BY  \nMalgorzata Trofimiuk-Muldner,  \nJagiellonian University Medical College, Poland Wencai Liu,  \nShanghai Jiao Tong University, China  \n*CORRESPONDENCE  \nShengyin Liao  \n[jxmu_2005@163.com](jxmu_2005@163.com)  \nRECEIVED 14 November 2024  \nACCEPTED 25 August 2025  \nPUBLISHED 08 September 2025  \nCITATION  \nHuang L, He L, Chen R and Liao S (2025) A machine learning model for predicting bone and/or lung metastasis in differentiated  \nthyroid carcinoma: enhancing precision in risk stratification.  \nFront. Endocrinol. 16:1528392 .  \ndoi: 10.3389/fendo.2025.1528392  \nCOPYRIGHT  \n© 2025 Huang, He, Chen and Liao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning model for predicting bone and/or lung metastasis in differentiated thyroid carcinoma: enhancing precision in risk stratification  \nLibin Huang 1,2, Limei He 1,2, Ru Chen 3 and Shengyin Liao 1,2*  \n1 Department of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, Fujian, China, 2The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian, China, 3 Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China  \nBackground: Differentiated thyroid cancer (DTC) incidence is rapidly rising worldwide. While most cases have a favorable prognosis, a subset of patients develop aggressive disease with distant metastases, particularly to the bone and lung, which significantly worsens outcomes. Current prediction models are limited in accuracy, often relying on basic clinical factors. This study aims to develop a machine learning model to improve prediction of bone and lung metastasis in DTC, enhancing risk stratification and early intervention.  \nMethods: Using the SEER database, we developed several machine learning models—including XGBoost, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, and Classification and Regression Trees (CART)—to predict bone and lung metastasis risk in DTC patients. LASSO regression was applied to select key predictive variables, and SMOTE was used to address data imbalance. The model’s generalizability was evaluated using an external validation cohort from China.  \nResults: The XGBoost model demonstrated the highest performance, achieving an AUC of 0 .988. Key predictive variables identified and included in the model were tumor size, radiation therapy, surgical interventions, histologic types, T and N stages, laterality, race, and household income. SHAP analysis confirmed the importance of these variables, with tumor size, radiation, and surgery emerging as primary predictors. In the external validation cohort, the model achieved an AUC of 0 . 866, indicating reliable predictive capability across clinical settings. Conclusion: This model accurately predicts bone and lung metastasis risk in DTC, offering valuable clinical utility for risk stratification and supporting early intervention strategies to improve outcomes in high-risk patients.  \nKEYWORDS  \nthyroid cancer, bone metastasis, lung metastasis, SEER, machine learning  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \n1 Induction  \nThyroid cancer (TC) is one of the most rapidly increasing malignancies globally, with a notable rise in incidence over the past few decades (1, 2).Differentiated thyroid cancer (DTC) is the most common type of malignant thyroid tumor, origina","cbCaip17Tajd2OUt","https://ap.wps.com/l/cbCaip17Tajd2OUt","pdf",4254907,1,11,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Key predictive variables\n## Model performance and validation","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets the need for more accurate prediction of bone and lung metastasis risk in differentiated thyroid cancer to improve early identification of high-risk patients.\"},{\"question\":\"Which machine learning methods are used to build the model?\",\"answer\":\"Models include XGBoost, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, and CART, with LASSO for variable selection and SMOTE for handling imbalance.\"},{\"question\":\"How well does the best model perform in training and external validation?\",\"answer\":\"The XGBoost model achieved an AUC of 0.988, and in an external validation cohort it achieved an AUC of 0.866, supporting reliable predictive capability.\"}]","A machine learning model for predicting bone and/or lung metastasis in differentiated thyroid carcinoma - 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