The accuracy of breast examinations and ultrasonography performed clinically to detect breast mass varies greatly depending on the physician's skill level, and the accuracy of breast examinations by non-experts is particularly low. In this study, we aimed to validate whether the concurrent use of ultrasound sensor technology is an efficient strategy for the purpose of improving the sensitivity of detecting breast masses through breast examination.

[Background] This research team would like to conduct this study based on the idea that the sensitivity of breast palpation can be improved by moving away from traditional breast palpation, which is simply performed by hand, and using auxiliary examination equipment based on ultrasonic sensor technology. In particular, our research team focused on the waveform of the ultrasound itself rather than the visual images obtained through the ultrasound device. In the existing breast ultrasound, the medical staff reads images created through ultrasound from multiple sensors to confirm the possibility of breast cancer, and this is read based on the medical staff's very subjective opinions. However, ultrasonic waveforms acquired through ultrasound can store information about the waveform as data and thus be implemented as objective values.

[Study design] Prospective, multi-institutional

[Study protocol]

① Preoperative ultrasound sensor-based diagnostic equipment was applied to 200 patients with breast mass among patients admitted to the breast surgery department, and prospectively obtained ultrasound echo signal data generated by the mass.

② For this purpose, the researcher uses equipment containing a single ultrasound sensor to manually scan the mass lesion area and no evidence disease area.

③ Diagnostic performance (judgment for presence or absence of a tumor) of diagnostic tool based on ultrasound sensor technology through an artificial intelligence algorithm designed based on ultrasound wavelength and frequency optimized for mass detection.

[Objectives]

1. Primary endpoint Sensitivity/specificity/predictive value/accuracy/positive & negative predictive of diagnostic performance
2. Secondary endpoint Artificial intelligence algorithm efficacy

Inclusion Criteria:

• female patients between 18 and 80 years of age who are scheduled for surgery after a tumor has been confirmed on breast ultrasound examination

Exclusion Criteria:

• Patients diagnosed with breast cancer after biopsy with non-mass enhancement or calcification
• Inflammatory breast cancer
• Patients whose cancer has invaded the skin and broken through
• Patients with skin diseases
• Women who refused to participate in the study

scheduled for surgery after a tumor has been confirmed on breast ultrasound examination