Clinical trial · Observational
Artificial Intelligence Analysis for Magnetic Resonance Imaging in Screening Breast Cancer in High-risk Women
Peking University People's Hospital Breast Center
NCT04996615CI-TRIAL-00062212unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Magnetic Resonance Imaging | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| no intervention | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- high risk population
- description
- women at high risk of breast cancer undergoing enhanced MRI
- interventionNames
- Other: no intervention
Primary outcomes (1)
- measure
- screening yield
- timeFrame
- 5 years
- description
- 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 outcomes (1)
- measure
- The accuracy of radiologists and deep learning models
- timeFrame
- 5 years
- description
- compare the sensitivity，specificity， positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models.
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
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
References
Publications (0)
Data not yet available
No reference posted for this study.