Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Sponsor

Shantou Central Hospital (Other)

Overall Status

Recruiting

CT.gov ID

NCT06001528

Collaborator

Zhejiang Cancer Hospital (Other), Sichuan Cancer Hospital and Research Institute (Other), Shenshan Medical Center of Sun Yat-sen Memorial Hospital (Other), Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University (Other)

2,400

Enrollment

Location

67.9

Anticipated Duration (Months)

35.3

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, we intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, we retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively include to verify the accuracy and stability of our model.

Condition or Disease	Intervention/Treatment	Phase
Breast Cancer Lymph Node Metastasis

Study Design

Study Type:

Observational

Anticipated Enrollment :

2400 participants

Observational Model:

Cohort

Time Perspective:

Other

Official Title:

Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Actual Study Start Date :

Jan 1, 2021

Anticipated Primary Completion Date :

Dec 31, 2023

Anticipated Study Completion Date :

Aug 31, 2026

Arms and Interventions

Arm	Intervention/Treatment
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.
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.
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.

Outcome Measures

Primary Outcome Measures

Metabolic difference detection [From January 01, 2021 to December 31, 2021]
Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found.
Predictive model establishment [From January 01, 2022 to December 31, 2022]
Combined with preoperative imaging and pathological information, a predictive model of sentinel lymph node metastasis in breast cancer would be established based on the metabolic difference.
Predictive model validation [From January 01, 2023 to December 31, 2023]
Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

Female

Accepts Healthy Volunteers:

Inclusion Criteria: patients with pathologically confirmed breast cancer; no preoperative therapy including chemotherapy or endocrine therapy and no distant metastasis; underwent mastectomy or breast-conserving surgery with sentinel lymph node biopsy; patients who agreed to provide preoperative peripheral blood samples and had access to imaging, pathological and follow-up data for preoperative and postoperative evaluation of the disease.

Exclusion Criteria:previous neoadjuvant therapy; 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; cases with incomplete pathological data and follow-up data; pregnant women; other patients determined by the investigator to be ineligible for inclusion in the study.