A Deep Learning Model Integrating Ultrasound Images and Multidimensional Clinical Information for Differentiating Benign and Malignant Non-Mass Lesions

Background/Objectives: This study aims to develop a deep learning (DL) model integrating ultrasound images and multidimensional clinical information to improve the diagnostic accuracy of breast non-mass lesions (NMLs). Methods: A total of 794 multicenter retrospective cases of NMLs were selected, stratified, and randomly divided into a training set (635 cases) and validation set (159 cases) at an 8:2 ratio. Multidimensional clinical information (including age, reproductive history, menstrual history, medical history, and findings from palpating the lesions) was incorporated to develop a DL model integrating ultrasound images and clinical data. To evaluate the diagnostic performance of the DL model, the area under the curve (AUC), accuracy, specificity, and sensitivity were employed. Results: The diagnostic model for NMLs integrating ultrasound images and multidimensional clinical information achieved an AUC of 0.8520 (95% CI: 0.7898–0.9068), F1 score of 0.7563, accuracy of 0.8176, sensitivity of 0.7031, and specificity of 0.8947. Its performance was superior to that of the model using only ultrasound images (AUC 0.8520 vs. 0.7571). SHAP analysis evaluating the reasons for the improved performance revealed that palpation with indistinct margins, abnormal axillary nodes, and older age were the three features with the highest contribution to predicting malignant risk. Conclusions: The DL model integrating ultrasound images and multidimensional clinical information demonstrated promising diagnostic performance in differentiating benign and malignant breast NMLs, suggesting the complementary value of multidimensional clinical information in the differential diagnosis of NMLs, though the reported AUC of 0.8520 is a preliminary internal estimate that awaits external validation.