As the most common cancer expected to occur all over the world, breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging. S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms. E-breast is a software installed in US machines which automatically reveals tumor elastographic features. This multi-center study intends to further validate the diagnostic efficiency of S-detect and E-breast in opportunistic breast cancer screening populations in China. Our hypothesis is that S-detect and E-breast can increase the diagnostic accuracy and specificity as compared to routinely US examinations by doctors.

Inclusion Criteria:

• Female over 18 years of age;
• Had breast lesions detected by ultrasound.
• No clinical symptoms such as nipple discharge, while breast lesions were not palpable.
• Received breast surgery within one week of ultrasound examination.
• Agreed to participant in this study and signed informed consent.

Exclusion Criteria:

• Patients who had received a biopsy of breast lesion before the ultrasound examination.
• Patients who were pregnant or lactating.
• Patients who were undergoing neoadjuvant treatment.

Biologically Female

Asymptomatic female patients voluntarily asked for breast US examination in comprehensive hospitals for breast cancer screening.