Abstract
OBJECTIVE: To evaluate the accuracy and efficacy of a 2.5-dimensional deep learning (DL) model based on digital breast tomosynthesis (DBT) in predicting breast cancer. METHODS: Through a retrospective analysis of data from 361 patients with breast tumor lesions treated at Shandong Provincial Hospital Affiliated to Shandong First Medical University between 2018 and 2020, this study utilized deep convolutional neural networks (DCNN) to automatically extract key features from DBT images. By applying dimensionality reduction and feature fusion selection, a variety of machine learning predictive models based on a 2.5-dimensional feature set were constructed. Additionally, a comprehensive predictive model was developed by combining univariate and multivariate logistic regression analyses with clinical data. The model's performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, and accuracy rates. RESULTS: In the test set, DBT 2.5D deep learning-based logistic regression, LightGBM, multilayer perceptron, and comprehensive models achieved accuracies of 72.2%, 75.0%, 79.2%, and 80.6%; AUCs of 0.826, 0.756, 0.859, and 0.871; sensitivities of 63.8%, 70.2%, 80.9%, and 87.2%; specificities of 88.0%, 84.0%, 76.0%, and 68.0%; PPVs of 90.9%, 89.2%, 86.4%, and 83.7%; NPVs of 56.4%, 60.0%, 67.9%, and 73.9%; and F1 scores of 75.0%, 78.6%, 83.5%, and 85.4%, respectively. These results underscore the high efficiency and potential of DBT 2.5D deep learning models in breast cancer diagnosis, particularly the comprehensive model's superior performance across key metrics. CONCLUSION: The 2.5D deep learning model based on DBT shows good performance in preoperative breast cancer prediction, with its integration with clinical data further enhancing its effectiveness. The combination of deep learning and radiomics offers a viable approach for early breast cancer diagnosis, supporting the development of more accurate personalized diagnostic and treatment strategies.