Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
NCT ID: NCT06001528
Last Updated: 2023-09-28
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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RECRUITING
2400 participants
OBSERVATIONAL
2021-01-01
2026-08-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Discovering cohort
Discovering cohort was used for the discovery and screening of metabolic differences. Two groups were included-SLN+ group and SLN- group, meaning the breast cancer patients with/without sentinel lymph node metastasis respectively. Abundance and distribution of serum and tissue metabolites in this cohort of patients would be observed.
No interventions assigned to this group
Modeling cohort
Modeling cohort refer to the cohort of patients included for targeted metabolites detection. Two groups were included-SLN+ group and SLN- group. Abundance and distribution of targeted metabolites in this cohort of patients would be detected, and a predictive model would be established using the data of this cohort.
No interventions assigned to this group
Validation cohort
Validation cohort means a cohort of patients included to validate the prediction model established in the modeling stage. Patients of validation cohort will be enrolled from several different hospitals. Also, it included SLN+ group and SLN- group. Abundance and distribution of targeted metabolites in this cohort of patients would be detected, and the accuracy and stability of prediction model will be verified in this cohort.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* No preoperative therapy including chemotherapy or endocrine therapy
* No distant metastasis
* Underwent mastectomy or breast-conserving surgery with sentinel lymph node biopsy
* Agreed to provide preoperative peripheral blood samples
* Had access to imaging, pathological and follow-up data for preoperative and postoperative evaluation of the disease
Exclusion Criteria
* Presence of distant metastasis at time of diagnosis
* Primary malignancies other than breast cancer
* Bilateral breast cancer or previous contralateral breast cancer
* Undergo modified radical surgery for breast cancer without sentinel lymph node biopsy
* Incomplete pathological data and follow-up data
* Pregnancy and other conditions determined by the investigator to be ineligible for inclusion in the study
18 Years
FEMALE
No
Sponsors
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Zhejiang Cancer Hospital
OTHER
Sichuan Cancer Hospital and Research Institute
OTHER
Shenshan Medical Center of Sun Yat-sen Memorial Hospital
UNKNOWN
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Shantou Central Hospital
OTHER
Responsible Party
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Principal Investigators
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Xiaorong Lin, Dr.
Role: STUDY_DIRECTOR
Shantou Central Hospital
Hai Hu, Pro.
Role: PRINCIPAL_INVESTIGATOR
Zhejiang Cancer Hospital
Zhiyong Wu, Dr.
Role: PRINCIPAL_INVESTIGATOR
Shantou Central Hospital
Locations
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Shantou Central Hospital
Shantou, Guangdong, China
Countries
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Central Contacts
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Facility Contacts
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References
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Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
Xu Y, Su GH, Ma D, Xiao Y, Shao ZM, Jiang YZ. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther. 2021 Aug 20;6(1):312. doi: 10.1038/s41392-021-00729-7.
Zhou H, Zhu L, Song J, Wang G, Li P, Li W, Luo P, Sun X, Wu J, Liu Y, Zhu S, Zhang Y. Liquid biopsy at the frontier of detection, prognosis and progression monitoring in colorectal cancer. Mol Cancer. 2022 Mar 25;21(1):86. doi: 10.1186/s12943-022-01556-2.
Richard V, Davey MG, Annuk H, Miller N, Kerin MJ. The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer. Mol Cancer. 2022 Apr 4;21(1):95. doi: 10.1186/s12943-022-01506-y.
Chayakulkheeree J, Pungrassami D, Prueksadee J. Performance of breast magnetic resonance imaging in axillary nodal staging in newly diagnosed breast cancer patients. Pol J Radiol. 2019 Oct 18;84:e413-e418. doi: 10.5114/pjr.2019.89690. eCollection 2019.
Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: A comprehensive review. Clin Genet. 2019 Jun;95(6):643-660. doi: 10.1111/cge.13514. Epub 2019 Feb 27.
Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmacherther Elektrophysiol. 2022 Mar;33(1):34-41. doi: 10.1007/s00399-022-00839-x. Epub 2022 Feb 11.
Other Identifiers
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XR Lin
Identifier Type: -
Identifier Source: org_study_id
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