An artificial intelligence (AI) algorithm can efficiently and accurately predict the presence of cancer on breast ultrasound exams, even exceeding the performance of interpreting sonographers, according to research published online June 9 in the Journal of Digital Imaging.
A team of researchers led by Heqing Zhang of Sichuan University in Chengdu, China, trained a variety of convolutional neural networks (CNNs) using 5,000 breast ultrasound exams, including 2,500 malignant and 2,500 benign cases. They then assessed the performance of the models on a separate test set of 1,007 images, which included 788 benign and 219 malignant cases.
The InceptionV3 model yielded the best results, producing an area under the curve (AUC) of 0.905. The difference between the other models based on VGG16, ResNet50, and VGG19 was statistically significant (p < 0.05).
The researchers then compared the InceptionV3 model with sonographers who had performed and interpreted 683 breast ultrasound exams, including 493 benign and 190 malignant cases. In this analysis, the deep-learning model had an AUC of 0.913, better than the AUC of 0.846 by the sonographers. This difference was also statistically significant (p < 0.05).
![Examples of ultrasound findings and techniques. (A) Images in a 39-year-old male patient with a mass in the left thigh. The mass is heterogeneous on the B-mode US image (compared with the patient in D) and showed increased microvascularity (superb microvascular imaging [SMI]) and shear-wave elastography (SWE) values. Undifferentiated pleomorphic sarcoma was diagnosed at biopsy (with pleomorphic rhabdomyosarcoma in surgical specimen). (B) Images in an 18-year-old male patient with a mass in the left leg. The mass is hypoechoic on the B-mode image, with no other findings suggestive of malignancy. The lesion is in contact with the cortex of the tibia, which is slightly irregular. CT revealed a doubtful anteromedial tibial erosion. The microvascular study demonstrated high vascularization, suggestive of malignancy. Periosteal Ewing sarcoma was diagnosed with both histologic and immunohistochemical confirmation. (C) Images in a 69-year-old female patient with a lump growing on the outside of the left leg. Multiple SWE examinations were performed (please note the high values obtained in the measurements, whereas the color map highlights the stiffness relative to adjacent tissues). SMI showed areas of increased vascularization to target for sampling. Undifferentiated spindle cell sarcoma was diagnosed at biopsy, with residual leiomyosarcoma in the surgical specimen after neoadjuvant therapy. (D) Images in a 56-year-old female patient with a mass in the right thigh. The mass is heterogeneous at both B-mode ultrasound (similar to patient A) and MRI (coronal T2-weighted spectral attenuated inversion recovery [SPAIR]; T1-weighted pre-contrast and postcontrast imaging), which even shows uptake after the administration of paramagnetic contrast material, which is traditionally suggestive of malignancy. Low values at SMI and elastography are suggestive of benignity. Spindle cell lipoma was diagnosed at biopsy, with atypical spindle cell lipomatous tumor in the surgical specimen.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/08/images-radiol250278fig2.APCFLSvX6p.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
