Artificial Intelligence Analysis for Magnetic Resonance Imaging in Screening Breast Cancer in High-risk Women

Recruiting

• Conditions: Magnetic Resonance Imaging; Breast Cancer
• Interventions: Other: no intervention

• Registration Number: NCT04996615
• Lead Sponsor: Peking University People's Hospital

Brief Summary

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 cancer in high-risk Chinese women, which can shorten the examination time and avoid enhanced imaging while ensuring the accuracy of the diagnosis.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-07-21
• Study First Submitted QC: 2021-07-30
• Study First Posted: 2021-08-09
• Study Start: 2021-09-01
• Status Verified: 2022-11
• Last Update Submitted: 2022-11-11
• Last Update Posted: 2022-11-16
• Primary Completion: 2025-09-30
• Study Completion: 2025-09-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 5000

Inclusion Criteria

• Patients undergoing full sequence BMRI examination
• Written informed consent and complete the clinical data questionnaire
• 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.
• Signs or symptoms of breast disease
• There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents.
• Patients during lactation or pregnancy

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
high risk population	no intervention	women at high risk of breast cancer undergoing enhanced MRI

Primary Outcome Measures

Name	Time	Method
screening yield	5 years	compare the rates of detection of breast cancers in the screening of high-risk populations between the Breast MRI full sequence, contrast-enhanced and non-contrast-enhanced sequence.

Secondary Outcome Measures

Name	Time	Method
The accuracy of radiologists and deep learning models	5 years	compare the sensitivity，specificity， positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models.

Trial Locations

Locations (2)

Peking university people's hospital; 🇨🇳 Beijing, Bei Jing, China

Peking University People's Hospital; 🇨🇳 Beijing, Beijing, China