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

Peking University People's Hospital5,000 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

5,000

Conditions

Breast Neoplasms Magnetic Resonance Imaging

Interventions

MRIDIAGNOSTIC_TEST

undergoing enhanced MRI

Eligibility

Sex: FEMALE

Medical Language ↔ Plain English

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

Locations (1)

Peking university people's hospital

Beijing, Beijing Municipality, China

RECRUITING

Outcomes

Primary Outcomes

Breast Cancer Screening

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.

Time frame: 5 years

Secondary Outcomes

The accuracy of radiologists and deep learning models

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.

Time frame: 5 years

Health economics

Compare the examination time, reading time and cost of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences.

Time frame: 5 years

Central Contacts

Yi Wang, doctor

CONTACT

86+010-88325813 wangyi@pkuph.edu.cn

Data from ClinicalTrials.gov

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