XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy
PurposeThyroid ultrasound is a primary tool for screening thyroid nodules (TNs), but existing risk stratification systems have limitations. Nowadays, machine learning (ML) offers advanced capabilities to handle high-dimensional data and complex patterns. This study aimed to develop an ML model integ...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1639639/full |
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| author | Wenhan Li Wenhan Li Yajing Zhou Ziyu Luo Ziyu Luo Miao Tan Miao Tan Rui Yin Jianhui Li Jianhui Li |
| author_facet | Wenhan Li Wenhan Li Yajing Zhou Ziyu Luo Ziyu Luo Miao Tan Miao Tan Rui Yin Jianhui Li Jianhui Li |
| author_sort | Wenhan Li |
| collection | DOAJ |
| description | PurposeThyroid ultrasound is a primary tool for screening thyroid nodules (TNs), but existing risk stratification systems have limitations. Nowadays, machine learning (ML) offers advanced capabilities to handle high-dimensional data and complex patterns. This study aimed to develop an ML model integrating clinical data and ultrasound features to improve personalized prediction of TN malignancy.MethodsData from 2,014 patients with TNs (2018.01–2024.01) were retrospectively analyzed, with 1,612 in the training set and 402 in the test set. Features included demographic, ultrasound, and thyroid function indices. Random Forest (RF) and Lasso regression were used for feature selection. Furthermore, six ML models (KNN, Logistic Regression, RF, Classification Tree, SVM, and XGBoost) were developed and validated via 10-fold cross-validation, evaluating performance using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA).Results17 variables were influential factors for diagnosing TNs. All six models exhibited satisfactory predictive performance, with their accuracy ranging from 0.761 to 0.851 and AUC from 0.755 to 0.928. Among them, the XGBoost model demonstrated the best performance, achieving an AUC of 0.928, accuracy of 0.851, sensitivity of 0.933, and specificity of 0.650. Calibration curves showed strong agreement between predicted and observed malignancy probabilities, and DCA indicated net clinical benefit across a wide risk threshold range (0.2–0.9). Additionally, we have developed the model as a web-based calculator to facilitate its practical application.ConclusionsThe XGBoost model effectively integrates multi-modal data to predict TN malignancy, offering improved accuracy and clinical utility. |
| format | Article |
| id | doaj-art-1b89175d497e448fb1f5d4fa9308e01a |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-1b89175d497e448fb1f5d4fa9308e01a2025-08-20T03:34:40ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-07-011610.3389/fendo.2025.16396391639639XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancyWenhan Li0Wenhan Li1Yajing Zhou2Ziyu Luo3Ziyu Luo4Miao Tan5Miao Tan6Rui Yin7Jianhui Li8Jianhui Li9Department of Surgical Oncology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, ChinaThe Third Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of Thyroid and Breast Surgery, The First Affiliated Hospital of Henan Polytechnic University (The Second People’s Hospital of Jiaozuo City), Jiaozuo, Henan, ChinaDepartment of Surgical Oncology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, ChinaThe Third Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of Surgical Oncology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, ChinaThe Third Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaDepartment of General Surgery Ward 1, Hospital of Ningshan County, Ankang, Shaanxi, ChinaDepartment of Surgical Oncology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, ChinaThe Third Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaPurposeThyroid ultrasound is a primary tool for screening thyroid nodules (TNs), but existing risk stratification systems have limitations. Nowadays, machine learning (ML) offers advanced capabilities to handle high-dimensional data and complex patterns. This study aimed to develop an ML model integrating clinical data and ultrasound features to improve personalized prediction of TN malignancy.MethodsData from 2,014 patients with TNs (2018.01–2024.01) were retrospectively analyzed, with 1,612 in the training set and 402 in the test set. Features included demographic, ultrasound, and thyroid function indices. Random Forest (RF) and Lasso regression were used for feature selection. Furthermore, six ML models (KNN, Logistic Regression, RF, Classification Tree, SVM, and XGBoost) were developed and validated via 10-fold cross-validation, evaluating performance using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA).Results17 variables were influential factors for diagnosing TNs. All six models exhibited satisfactory predictive performance, with their accuracy ranging from 0.761 to 0.851 and AUC from 0.755 to 0.928. Among them, the XGBoost model demonstrated the best performance, achieving an AUC of 0.928, accuracy of 0.851, sensitivity of 0.933, and specificity of 0.650. Calibration curves showed strong agreement between predicted and observed malignancy probabilities, and DCA indicated net clinical benefit across a wide risk threshold range (0.2–0.9). Additionally, we have developed the model as a web-based calculator to facilitate its practical application.ConclusionsThe XGBoost model effectively integrates multi-modal data to predict TN malignancy, offering improved accuracy and clinical utility.https://www.frontiersin.org/articles/10.3389/fendo.2025.1639639/fullthyroid nodulesmachine learningXGBoostdiagnosisweb-based calculator |
| spellingShingle | Wenhan Li Wenhan Li Yajing Zhou Ziyu Luo Ziyu Luo Miao Tan Miao Tan Rui Yin Jianhui Li Jianhui Li XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy Frontiers in Endocrinology thyroid nodules machine learning XGBoost diagnosis web-based calculator |
| title | XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy |
| title_full | XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy |
| title_fullStr | XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy |
| title_full_unstemmed | XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy |
| title_short | XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy |
| title_sort | xgboost based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy |
| topic | thyroid nodules machine learning XGBoost diagnosis web-based calculator |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1639639/full |
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