Development of a prediction model for the association between thyroid dysfunction and breast cancer.

Background: Thyroid dysfunction is associated with the risk of benign and malignant breast tumors, but currently there is a lack of model studies to demonstrate the predictive role of thyroid dysfunction in benign and malignant breast tumors. This study aims to establish a model for predicting the association between thyroid dysfunction and breast cancer. Methods: This retrospective study enrolled breast tumor patients from the Affiliated Tumor Hospital of Xinjiang Medical University from 2015 to 2019. Their baseline data and laboratory data were collected. Python was used for data processing and analysis. Data preparation, feature selection, model construction, and model evaluation were conducted. We utilized the classification probabilities generated by the model as scores and further conducted a least absolute shrinkage and selection operator analysis. Results: Analysis of the laboratory data revealed statistically significant differences in thyroid-stimulating hormone, thyroxine, free thyroxine, free triiodothyronine, and thyronine levels between patients with benign and malignant tumors. Based on age, ethnicity, thyroid function, and estrogen levels, the predictive model for breast tumor malignancy indicated that the factors with the greatest importance ranking were age > follicle-stimulating hormone > luteinizing hormone > prolactin > thyroxine > testosterone > ethnicity. The model showed an accuracy rate of 83.70%, precision of 90.69%, sensitivity of 84.74%, and specificity of 81.50%. The area under the receiver operating characteristic curve was 0.9012, close to 1, indicating good predictive performance of the model. Conclusions: The predictive model based on factors such as age, ethnicity, thyroid function, and estrogen levels performs well in predicting the occurrence and development of benign and malignant breast tumors.