Multi-center Study of Deep Learning AI in Breast Mass

June 29, 2022 updated by: Cancer Institute and Hospital, Chinese Academy of Medical Sciences

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Anticipated)

1122

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Beijing
      • Beijing, Beijing, China, 100021
        • Recruiting
        • National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

Female

Sampling Method

Probability Sample

Study Population

Femal patients with breast neoplasm

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Collaborators

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

Investigators

  • Principal Investigator: Yong Wang, Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 12, 2021

Primary Completion (Anticipated)

August 31, 2022

Study Completion (Anticipated)

August 31, 2023

Study Registration Dates

First Submitted

June 29, 2022

First Submitted That Met QC Criteria

June 29, 2022

First Posted (Actual)

July 5, 2022

Study Record Updates

Last Update Posted (Actual)

July 5, 2022

Last Update Submitted That Met QC Criteria

June 29, 2022

Last Verified

June 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • NCC2962

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Breast Neoplasms

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Singapore

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe