Prediction of Axillary Lymph Node Metastasis Status in Breast Cancer Based on PET/CT Radiomics
NCT ID: NCT05826197
Last Updated: 2023-05-09
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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UNKNOWN
100 participants
OBSERVATIONAL
2023-05-10
2024-12-31
Brief Summary
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Image segmentation: Image segmentation was performed using ITK-SNAP software (4) (version 3.6.0, http://www.itksnap.org/), Brush Style: circular, Brush Size: 10, Brush Options: 3D. The entire tumor volume was outlined on the PET image as ROI for segmentation.
An open source Python package (PyRadiomics version 3.0.1(5)) was used to extract the radiomics features from the ROI.
Univariate and multivariate binary logistic regressions were used to construct model for predicting lymph node metastasis in breast cancer.
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
PROSPECTIVE
Study Groups
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Axillary lymph node metastasis
Radiomics
PET Radiomics
No axillary lymph node metastasis
No interventions assigned to this group
Interventions
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Radiomics
PET Radiomics
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
80 Years
FEMALE
No
Sponsors
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First Affiliated Hospital Xi'an Jiaotong University
OTHER
Responsible Party
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Central Contacts
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References
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Li Y, Han D, Shen C. Prediction of the axillary lymph-node metastatic burden of breast cancer by 18F-FDG PET/CT-based radiomics. BMC Cancer. 2024 Jun 7;24(1):704. doi: 10.1186/s12885-024-12476-3.
Li Y, Han D, Shen C, Duan X. Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study. BMC Cancer. 2023 Oct 24;23(1):1028. doi: 10.1186/s12885-023-11498-7.
Other Identifiers
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2023-YBSF-480
Identifier Type: -
Identifier Source: org_study_id
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