Clinical trial NCT05443672: Multi-center Study of Deep Learning AI in Breast Mass
Official title: A Multi-center Study of Breast Mass Screening and Diagnosis Using Deep Learning AI-based on Real-time Ultrasound Examination
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Breast Neoplasms
Lead sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Sponsor class: OTHER
Collaborators: name: Peking Union Medical College Hospital
name: Peking University Cancer Hospital & Institute
name: Peking University Third Hospital
name: Guangdong Provincial Hospital of Traditional Chinese Medicine
name: Third Affiliated Hospital of Zhengzhou University
name: Hebei Medical University Fourth Hospital
name: Henan Cancer Hospital
name: Shanxi Province Cancer Hospital
name: First Affiliated Hospital Xi'an Jiaotong University
name: Chongqing University Cancer Hospital
name: Anqing Hospital affiliated to Anhui Medical University
name: Qinhuangdao Maternal and Child Health Care Hospital
name: The First Affiliated Hospital of Xiamen University
name: Anyang Tumor Hospital
name: The Third Affiliated Hospital of Jinzhou Medical University
name: General Hospital of Jincheng Coal Industry Group
name: Suzhou First People's Hospital
name: Peking University
First posted: 2022-07-05
Enrollment: 1122
Sex: FEMALE
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Femal patients with breast neoplasm

Brief 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination
timeFrame: 12 months
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
state: Beijing
Study officials:
affiliation: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
name: Yong Wang
role: PRINCIPAL_INVESTIGATOR
References: