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Updated: Jun 12, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Deep learning-based Breast Imaging Reporting and Data System classification and establishment of diagnostic model in
Xinyi Zhou1, Mingwang Xu2, Gongning Luo2
1Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.
Background:
Deep learning (DL) holds substantial promise for ultrasonography interpretation. However, there is limited research on the differential diagnosis of tumors in automated breast ultrasound (ABUS). This study constructs a breast cancer diagnosis model based on DL-adjusted Breast Imaging Reporting and Data System (D-BI-RADS) and determines its diagnostic performance.
Methods:
This retrospective study evaluated 786 patients with 832 breast nodules who underwent ABUS examination, follow-up or surgery from November 2018 to January 2022. The three-dimensional (3D) Vision Transformer (ViT) DL diagnostic models (DMs) were established and ABUS BI-RADS was adjusted to D-BI-RADS. With univariable and multivariable logistic regression, incorporating ABUS features and D-BI-RADS, three breast cancer DMs were constructed. Area under the receiver operating characteristic curve (AUC) was compared between DL, ABUS BI-RADS, and D-BI-RADS. In addition, the three DMs were verified in the validation set and test set.
Results:
The training, validation, and test data sets included 599 [mean age, 55±1.41 years; 351 (58.5%) malignant], 67 [mean age, 47.5±0.71 years; 40 (59.7%) malignant], and 166 [mean age, 61.5±4.95 years; 96 (57.8%) malignant] nodules. In the training data set, the D-BI-RADS showed improved performance in breast cancer diagnosis [AUC, 0.82; 95% confidence interval (CI): 0.79-0.85; P<0.01] compared with A-BI-RADS. With BI-RADS category 4b (3.5) as the cut-off value for benign and malignant diagnosis, the D-BI-RADS showed improved performance (AUC, 0.919; 95% CI: 0.896-0.941). In addition, Logistic, Random Forest, and Support Vector Machine (SVM) methods achieved ideal performance in the test set (AUC, 0.93; 95% CI: 0.89-0.97; AUC, 0.934; 95% CI: 0.897-0.972; AUC, 0.938; 95% CI: 0.901-0.974; respectively).
Conclusions:
The DL can be a useful adjunct guide to the adjustment of BI-RADS, and the established DMs achieved ideal diagnostic performance in breast cancer.
