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

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Breast Cancer; Breast Neoplasm; Ultrasonography; Artificial Intelligence; Benign Breast Disease

Detailed 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

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

Study Locations

• Korea, Republic of
  • Seoul, Korea, Republic of, 03080
    • Seoul National University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Ultrasonic group

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Time Frame
Artificial intelligence algorithm efficacy	Within 2 year after the study participant registration deadline

Collaborators and Investigators

• Sponsor: Seoul National University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): October 5, 2024
• Primary Completion (Estimated): February 27, 2025
• Study Completion (Estimated): April 5, 2025

Study Registration Dates

• First Submitted: February 4, 2024
• First Submitted That Met QC Criteria: February 4, 2024
• First Posted (Actual): February 13, 2024

Study Record Updates

• Last Update Posted (Estimated): December 4, 2024
• Last Update Submitted That Met QC Criteria: December 3, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Neoplasms; Breast Diseases; Fibrocystic Breast Disease
• Other Study ID Numbers: 2209-039-1357

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No