Artificial Intelligence Analysis for Magnetic Resonance Imaging in Screening and Diagnosis of Breast Cancer

February 7, 2022 updated by: WangYi, Peking University People's Hospital

Peking University People's Hospital Radiology

Use Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Build an abbreviated protocal, and investigate whether an abbreviated protocol was suitable for breast magnetic resonance imaging screening for breast mass in Chinese women, which can shorten the examination time and avoid enhanced imaging while ensuring the accuracy of the diagnosis.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Anticipated)

5000

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, 100044
        • Recruiting
        • Peking University People's Hospital
        • 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

Non-Probability Sample

Study Population

Clinically symptomatic patients (defined as palpable masses, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes according to the guidelines) should be examined by BMRI at the judgment of the clinician.

Description

Inclusion Criteria:

  • Patients with clinical symptoms (define as palpable mass, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes)
  • Patients undergoing full sequence BMRI examination
  • Through the follow-up database, at least 6 months of follow-up results can be obtained to determine whether the diagnosis result is negative/benign/malignant; for patients who need pathological biopsy, the pathological biopsy results shall prevail to determine the lesion benign/malignant

Exclusion Criteria:

  • The breast had received radiotherapy, chemotherapy, biology and other treatments before BMRI.
  • There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents.
  • A prosthesis is implanted in the affected breast.
  • Patients during lactation or pregnancy

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Clinically symptomatic patients
Clinically symptomatic patients (defined as palpable masses, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes according to the guidelines) should be examined by BMRI at the judgment of the clinician.
Diagnostic test: MRI
undergoing enhanced MRI

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Breast Cancer Screening
Time Frame: 5 years
Compare the area under the curve of the deep learning model of the BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequence in the diagnosis of breast cancer.
5 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The accuracy of radiologists and deep learning models
Time Frame: 5 years
Under the conditions of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences, compare the sensitivity, specificity, positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models.
5 years
Health economics
Time Frame: 5 years
Compare the examination time, reading time and cost of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences.
5 years

Collaborators and Investigators

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

Sponsor

Peking University People's Hospital

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 (Anticipated)

February 28, 2022

Primary Completion (Anticipated)

September 30, 2025

Study Completion (Anticipated)

September 30, 2025

Study Registration Dates

First Submitted

September 12, 2021

First Submitted That Met QC Criteria

February 7, 2022

First Posted (Actual)

February 16, 2022

Study Record Updates

Last Update Posted (Actual)

February 16, 2022

Last Update Submitted That Met QC Criteria

February 7, 2022

Last Verified

February 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-BMRI-S

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.

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