Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor

Breast Cancer Clinical Trial

Official title:

Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor : A Development and Validation Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06255808
• Other study ID #: 2209-039-1357
• Status: Recruiting
• Start date: October 5, 2022
• Est. completion date: January 5, 2025

Study information

• Verified date: April 2024
• Source: Seoul National University Hospital
• Contact: Hyeong-Gon Moon, MD
• Phone: 02-2072-2634
• Email: moonhgsurgi[@]gmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

[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

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: January 5, 2025
• Est. primary completion date: January 5, 2025
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

• Artificial Intelligence
• Benign Breast Disease
• Breast Cancer
• Breast Diseases
• Breast Neoplasm
• Breast Neoplasms
• Ultrasonography

Locations

Country	Name	City	State
Korea, Republic of	Seoul National University Hospital	Seoul

Sponsors (1)

Lead Sponsor	Collaborator
Seoul National University Hospital

Country where clinical trial is conducted

Korea, Republic of, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Device performance	Sensitivity/specificity/predictive value/accuracy/positive predictive value/ negative predictive value of diagnostic performance	Within 1 year after the study participant registration deadline
Secondary	Artificial intelligence algorithm efficacy		Within 2 year after the study participant registration deadline