Background and purpose: Digital breast tomosynthesis (DBT) has been applied to breast cancer screening and diagnosis population

which can improve the breast cancer detection. The purpose of this study was to evaluate the differential diagnosis of breast mass lesions based on DBT images. Methods: In the retrospective study

we analyzed the patients undergoing DBT examination in Fudan University Shanghai Cancer Center between April 2019 and August 2020

who were confirmed by surgery and pathology. Finally

a total of 143 female patients showing mass signs were enrolled in this study. Radiomics features were extracted from 3D images of DBT based on mass lesions

and Lasso logistic regression model was used for feature dimension reduction and screening to establish radiomics labels. The model was built by logistic regression (LR)

support vector machine (SVM) and gradient boosting decision tree (GBDT) algorithms. Receiver operating characteristic (ROC) was used to evaluate the diagnostic efficacy of radiomic labels for benign and malignant breast tumors. Results: Among 144 lesions confirmed by pathology

65 were benign and 79 were malignant. It was divided into training set and test set according to the ratio of 8∶2. Based on the classifier algorithm with different number features

the optimal numbers of features of LR

SVM and GBDT were 20

24 and 32 respectively. The GBDT model achieved an area under curve (AUC) value of 0.91 on the test set. Conclusion: Due to the advantages of integrated learning

GBDT model based on radiomics could effectively distinguish benign from malignant breast lesions in DBT.