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

Breast Cancer Clinical Trial

Official title:

A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03887598
• Other study ID #: 2019(S073)
• Status: Recruiting
• Start date: January 18, 2019
• Est. completion date: February 18, 2020

Study information

• Verified date: March 2019
• Source: Tongji Hospital
• Contact: Li-Qiang Zhou, MD
• Phone: 15387076275
• Email: zlq_1118[@]hust.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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 information / eligibility

• Status: Recruiting
• Enrollment: 2000
• Est. completion date: February 18, 2020
• Est. primary completion date: January 18, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Breast Cancer

Intervention

Device:
• Ultrasound diagnosis
Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology

Locations

Country	Name	City	State
China	Xin-Wu Cui	Wuhan	Hubei

Sponsors (22)

Lead Sponsor

Collaborator

Xin-Wu Cui

Affiliated Hospital of Jiangsu University, Anqing People's Hospital, Enshi State Central Hospital, First People's Hospital of Jiangxia District, Wuhan City, Huai'an First People's Hospital, Huainan People's Hospital, Huangshi Central Hospital, Lianyungang Third People's Hospital, Macheng People's Hospital, Taizhou Hospital, The Central Hospital of Lishui City, The First People's Hospital of Yichang, the Second Affiliated Hospital of Anhui Medical University, The Second People's Hospital of Yichang, Wenzhou Central Hospital, WISCO General Hospital, Wuhan Hospital of Traditional Chinese and Western Medicine, Xiangyang Central Hospital, Xiangyang First People's Hospital, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Xuzhou First People's Hospital

Country where clinical trial is conducted

China, 

References & Publications (4)

Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography. 2018 Jul;37(3):217-225. doi: 10.14366/usg.17046 — View Citation

Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: vali — View Citation

Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017 Jan;36(1):3-9. doi: 10.14366/usg. — View Citation

Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013 Oct;43(10):1563-72. doi: 10.1016/j.compbiomed.2013.08.003. Epub 2013 Aug 19. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Benign or malignant lesions as determined by pathology	The pathological diagnosis of benign or malignant lesions from surgery samples	Before surgery or biopsy
Primary	Elastic ratio	Clear ECI value	Before surgery or biopsy