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Application of Ultrasound Artificial Intelligence and Elastography in Differential Diagnosis of Breast Nodules

Conditions
Breast Cancer
Interventions
Device: Ultrasound diagnosis
Registration Number
NCT03887598
Lead Sponsor
Xin-Wu Cui
Brief Summary

The application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Index (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-Detect technology for benign and malignant breast nodules and evaluate the differential diagnosis consistency of the ultrasound S-Detect technique and the examiner for benign and malignant breast nodules and explore the differential diagnosis value of Samsung ultrasound elastic contrast Index (ECI) technique for benign and malignant breast nodules.

Detailed Description

Breast cancer is the most common malignancy in women and the second leading cause of cancer deaths worldwide. Therefore, early detection of breast cancer and timely treatment are of great significance for controlling and reducing breast cancer mortality. Breast ultrasound is an adjunct to extensive use in the detection of breast cancer, but ultrasound is highly technically dependent on the examiner, and the results are greatly influenced by the subjective nature of the examiner, adding unnecessary surgery and puncture, which causes great problems for clinicians and patients.Moreover, the value of conventional ultrasound in the differential diagnosis of breast mass is still limited, and the emergence of new technologies such as artificial intelligence and elastography has improved the accuracy of ultrasound diagnosis to varying degrees.

S-Detect technology is a computer-aided (CAD) system recently developed by Samsung Medical Center for breast ultrasound to assist in morphological analysis based on the Breast Imaging Reporting and Data System (BI-RADS) description and final assessment.This provides a new way to identify the benign and malignant breast nodules.

The E-Breast technique, unlike conventional strain-elastic imaging technology, performs an elastic analysis of the entire two-dimensional image.Moreover, when measuring the elastic ratio, it is only necessary to place a region of interest (ROI) at the nodule.Compared with the average elasticity of the surrounding area, it is more reflective of the elastic ratio of the mass to the surrounding tissue.

Recruitment & Eligibility

Status
UNKNOWN
Sex
Female
Target Recruitment
2000
Inclusion Criteria
  1. Had breast lesions detected by ultrasound
  2. Age 18 or older
  3. Upcoming FNAB or surgery
  4. Signing informed consent
Exclusion Criteria
  1. Patients who had received a biopsy of breast lesion before the ultrasound examination
  2. Can not cooperate with the test operation
  3. Patients who were pregnant or lactating
  4. Patients who were undergoing neoadjuvant treatment.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
breast noduleUltrasound diagnosisThose with one or more breast nodules, age 18 or older, upcoming FNAB or surgery and signed informed consent.Those without adverse effects on the test or threatening other candidates, such as mental illness, pregnancy, poor ultrasound image quality, history of breast surgery or breast biopsy, simple cystic nodules, calcification, excessive mass or too small, the S-DetectTM system can not identify the boundary of the tumor, the basic information is incomplete.
Primary Outcome Measures
NameTimeMethod
Elastic ratioBefore surgery or biopsy

Clear ECI value

Benign or malignant lesions as determined by pathologyBefore surgery or biopsy

The pathological diagnosis of benign or malignant lesions from surgery samples

Secondary Outcome Measures
NameTimeMethod

Trial Locations

Locations (1)

Xin-Wu Cui

🇨🇳

Wuhan, Hubei, China

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