Dual-modal ultrasound-based deep learning radiomics for differentiation of benign and malignant breast lesions

Breast cancer (BC) is the most prevalent malignancy among women worldwide. The development of accurate and noninvasive diagnostic methods is essential to reduce unnecessary biopsies and surgeries. This study aims to develop a dual-modal deep learning (DL) radiomics model based on B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images. The model is designed to assist radiologists in accurately differentiating benign from malignant breast lesions.

This retrospective multicenter study included 427 female patients with breast lesions from four hospitals. Traditional radiomics models were constructed using logistic regression (LR). DL radiomics models were built on a VGG-16 network pretrained on ImageNet. An integrated model was developed through early feature fusion of BUS and CEUS features. Model interpretability was assessed with Shapley Additive exPlanations (SHAP) and heatmaps generated by gradient-weighted class activation mapping (Grad-CAM). In a two-round reader study, the integrated model provided radiologists with artificial intelligence (AI) scores and heatmaps to support diagnosis. Model performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).

In the testing cohort, the integrated model achieved the highest performance, with an AUC of 0.825 [95% confidence interval (CI): 0.744-0.907]. SHAP analysis revealed that, compared with BUS features, CEUS features had a greater impact on the model's diagnostic performance. In the first round, the integrated model outperformed all the radiologists (model AUC: 0.825 vs. radiologists' AUCs: 0.701-0.824). In the second round, radiologists assisted by the integrated model demonstrated improved performance. Their AUCs ranged from 0.748 to 0.869, with ΔAUCs ranging from +0.030 to +0.058. Four radiologists outperformed the model itself.

The integrated model provides an effective and noninvasive approach for predicting the benignity or malignancy of breast lesions. It has a strong potential to serve as a valuable clinical tool for improving radiologists' diagnostic performance.