Enhanced value of chest computed tomography radiomics features in breast density classification.

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Abstract

This study investigates the correlation between chest computed tomography (CT) radiomics features and breast density classification, and aiming to develop an automated radiomics model for breast density assessment using chest CT images. The diagnostic performance was evaluated to establish a CT-based alternative for breast density classification in clinical practice. A retrospective analysis was conducted on patients who underwent both mammography and chest CT scans. The breast density classification results based on mammography images were used to guide the development of CT-based breast density classification models. Radiomic features were extracted from breast regions of interest (ROIs) segmented on chest CT images. The diagnostic performance was evaluated to establish a CT-based alternative for breast density classification in clinical practice. Following dimensionality reduction and selection of dominant radiomic features, four four-class classification models were established, including ① Extreme Gradient Boosting (XGBoost), ② One Vs Rest Classifier-Logistic Regression, ③ Gradient Boosting, and ④ Random Forest Classifier. The performance of these models in classifying breast density using CT images was then evaluated. A total of 330 patients, aged 23-79 years, were included for analysis. The breast ROIs were automatically segmented using a U-net neural network model and subsequently refined and calibrated manually. A total of 1427 radiomic features were extracted, and after dimensionality reduction and feature selection, 28 dominant features closely associated with breast density classification were obtained to construct four classification models. Among the tested models-XGBoost, One-vs-Rest Logistic Regression, Gradient Boosting Classifier, and Random Forest Classifier-the XGBoost model achieved the best performance, with a classification accuracy of 86.6%. Analysis of the receiver operating characteristic curves showed Area Under the Curve (AUC) values of 1.00, 0.93, 0.93, and 0.99 for the four breast density categories, along with a micro-averaged AUC of 0.97 and a macro-averaged AUC of 0.96. Chest CT scans, combined with imaging radiomics models, can accurately classify breast density, providing valuable information related to breast cancer risk stratification. The proposed classification model offers a promising tool for automated breast density assessment, which could enhance personalized breast cancer screening and clinical decision-making.

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