Ultrasound-based Diagnostic Model for Differentiating Malignant Breast Lesion From Benign Lesion
NCT ID: NCT03080623
Last Updated: 2020-10-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
1981 participants
OBSERVATIONAL
2018-09-08
2019-12-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Suspicious breast lesions
women with Suspicious breast lesions, who need to receive breast ultrasound will be collected in this cohort. According to the diagnosis of breast ultrasound，patients will be assigned to breast biopsy or follow-up
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* with single breast lesion(maximum diameter\>0.8 cm)
* agree to receive follow-up in six months and take the second breast ultrasound six month after the first breast ultrasound, if a benign diagnosis is achieved at the first ultrasound
* sign the informed consent
Exclusion Criteria
* failed to take the second breast ultrasound six month after the first breast ultrasound
18 Years
80 Years
FEMALE
No
Sponsors
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Peking University
OTHER
Responsible Party
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Tao OUYANG
Chairman of Breast Center of Beijing Cancer Hospital
Principal Investigators
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Tao Ouyang
Role: STUDY_CHAIR
Peking University Cancer Hospital & Institute
Locations
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Peking University People's Hospital
Beijing, Beijing Municipality, China
Haidian women and children's hospital of beijing
Beijing, , China
Shunyi women and children's hospital of Beijing Children's Hospital
Beijing, , China
Fourth Hospital of Heibei Medical Hospital
Shijiazhuang, , China
Countries
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References
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Huo L, Tan Y, Wang S, Geng C, Li Y, Ma X, Wang B, He Y, Yao C, Ouyang T. Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study. Cancer Manag Res. 2021 Apr 16;13:3367-3379. doi: 10.2147/CMAR.S297794. eCollection 2021.
Other Identifiers
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D161100000816006
Identifier Type: -
Identifier Source: org_study_id