Assessing Diabetes-Associated Bone Changes Using Chest Computed Tomography Radiomics and Extreme Gradient Boosting: A Retrospective Study

Chest computed tomography (CT) provides quantitative information on bone that may reveal diabetes-associated skeletal changes not readily identified by routine image assessment. This study aimed to establish a prediction model for diabetes using chest CT radiomics features and identify bone features with the highest contributions to diabetes prediction. One hundred patients with diabetes and 100 healthy controls underwent chest CT. The thoracic vertebrae, scapulae, and sternum were automatically segmented using a convolutional neural network (CNN), and 93 radiomics features were extracted from each structure. Max-Relevance and Min-Redundancy (mRMR), least absolute shrinkage and selection operator (LASSO), and extreme gradient boosting (XGBoost) were successively used for feature selection and model development. The area under the receiver operating characteristic curve (AUC) was evaluated using internal-validation data and an external-test cohort of 50 participants, including 25 patients with diabetes. Shapley Additive Explanations (SHAP) were used to interpret the XGBoost model. After mRMR, and LASSO, 17 features were selected. The model achieved AUCs of 0.95 and 0.90 on the internal-validation and external-test data, respectively. gldm_SmallDependenceLowGrayLevelEmphasis_T11, glcm_Idmn_T8, and gldm_LargeDependenceHighGrayLevelEmphasis_T4 showed the highest contributions to diabetes prediction in the SHAP analysis. Glycated hemoglobin (HbA1c) levels correlated with several bone CT features. These findings demonstrate that chest CT radiomics combined with interpretable machine learning can characterize diabetes-associated bone changes and distinguish patients with diabetes from healthy controls.