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

Study Overview
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Brief Summary
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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.

Official Title
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Peking University People's Hospital Radiology

Conditions
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Breast Neoplasms, Magnetic Resonance Imaging

Intervention / Treatment
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* Diagnostic Test: MRI


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Clinically symptomatic patients<br>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<br>* undergoing enhanced MRI<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 5 years | 
| Health economics | Compare the examination time, reading time and cost of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences. | 5 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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breast neoplasms, magnetic resonance imaging, screening, deep learning, machine learning