Multi-center Study of Deep Learning AI in Breast Mass

Breast Neoplasms Clinical Trial

Official title:

A Multi-center Study of Breast Mass Screening and Diagnosis Using Deep Learning AI-based on Real-time Ultrasound Examination

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05443672
• Other study ID #: NCC2962
• Status: Recruiting
• Start date: August 12, 2021
• Est. completion date: August 31, 2023

Study information

• Verified date: June 2022
• Source: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
• Contact: Yong Wang
• Phone: 13391817899
• Email: drwangyong77[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.

Description:

As the most common cancer expected to occur all over the world, extensive population screening plays a very important role in the early diagnosis and prognosis of the breast cancer. X-ray and ultrasound are the most commonly used screening methods, and ultrasound is especially important for Asian women with dense breasts. However, ultrasound is greatly affected by the operator's skill and experience, and the diagnostic accuracy varies greatly.

Artificial intelligence (AI) is a new method emerging in recent years, active in many medical fields and can effectively improve the diagnostic efficiency. However, previous researches on the application of AI in ultrasound are focused on single or multi-modality static ultrasound images. This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1122
• Est. completion date: August 31, 2023
• Est. primary completion date: August 31, 2022
• Accepts healthy volunteers: No
• Gender: Female
• Age group: N/A and older

Eligibility

Inclusion Criteria:

1. Females who undergo ultrasound examination for a complaint of breast lesion;

2. The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years.

Exclusion Criteria:

1. The breast lesion that has received CNB or FNA;

2. The breast cancer patient who has received neoadjuvant chemotherapy.

Related Conditions & MeSH terms

• Breast Neoplasms

Intervention

Device:
• Yizhun BUSMS
During the breast scanning, Yizhun BUSMS uses different color box to identify the breast lesion, and the box color indicates the risk grade of the lesion.

Locations

Country	Name	City	State
China	National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College	Beijing	Beijing

Sponsors (19)

Lead Sponsor

Collaborator

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Anqing Hospital affiliated to Anhui Medical University, Anyang Tumor Hospital, Beijing Cancer Hospital, Chongqing University Cancer Hospital, First Affiliated Hospital Xi'an Jiaotong University, General Hospital of Jincheng Coal Industry Group, Guangdong Provincial Hospital of Traditional Chinese Medicine, Hebei Medical University Fourth Hospital, Henan Cancer Hospital, Peking Union Medical College Hospital, Peking University, Peking University Third Hospital, Qinhuangdao Maternal and Child Health Care Hospital, Shanxi Province Cancer Hospital, Suzhou First People's Hospital, The First Affiliated Hospital of Xiamen University, The Third Affiliated Hospital of Jinzhou Medical University, Third Affiliated Hospital of Zhengzhou University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination	Pathology as a gold standard, to evaluate the diagnostic performance (sensitivity, specificity and accuracy)	12 months