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

Peking University People's Hospital Breast Center

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04996615

Enrollment

5000

Registered

2021-08-09

Start date

2021-09-01

Completion date

2025-09-30

Last updated

2022-11-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Cancer, Magnetic Resonance Imaging

Keywords

breast cancer, screening, high risk women, magnetic resonance imaging, deep learning

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.

Interventions

OTHERno intervention

no intervention

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

FEMALE

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
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

Measure	Time frame	Description
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.

Countries

China

Contacts

Primary Contactshu wang, doctor

shuwang@pkuph.edu.cn86+010-88324010

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026