ArticleBMJ open2025
Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning model incorporating spectral CT quantitative parameters and clinical characteristics for differentiation of benign and malignant thyroid nodules.BMC medical imaging · 2025Article
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios.BMC endocrine disorders · 2025Article
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9 authors.
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Abstract
objectivesTo develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.
settingA qualified panel of 1649 patients with TNs from one hospital were stratified by gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibody (TPOAB).
participantsThyroid function (TF) data of 1649 patients with TNs were collected in a single centre from January 2018 to June 2022, with a total of 273 males and 1376 females, respectively. MEASURES: Seven popular ML models (Random Forest, Decision Tree, Logistic Regression (LR), K-Neighbours, Gaussian Naive Bayes, Multilayer Perception and Gradient Boosting) were developed to predict malignant and benign TNs, whose performance indicators included area under the curve (AUC), accuracy, recall, precision and F1 score.
resultsA total of 1649 patients were enrolled in this study, with the median age of 45.15±13.41 years, and the male to female ratio was 1:5.055. In the multivariate LR analysis, statistically significant differences existed between the TNs group and thyroid cancer group in gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and TPOAB. Among the seven tested ML models, the best performance was achieved in the Gradient Boosting model in terms of precision, AUC, accuracy, recall and F1 score, with the AUC of 0.82, accuracy of 79.4% and precision of 0.814 after experimental verification. FT4, TPOAB and FT3 were validated as the top three features in the Gradient Boosting model.
conclusionsThis study innovatively developed a predictive model for benign and malignant TNs based on the Gradient Boosting Decision Tree algorithm. For the first time, it validated the clinical predictive value of TF parameters (FT4, FT3) and TPOAB as key biomarkers.
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