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

Study Overview
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Brief Summary
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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, the investigators 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, the investigators 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 be included to verify the accuracy and stability of the model.

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

Conditions
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Breast Cancer, Lymph Node Metastasis

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Pathological diagnosis of breast cancer 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: 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 Incomplete pathological data and follow-up data Pregnancy and other conditions determined by the investigator to be ineligible for inclusion in the study

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Discovering cohort<br>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<br>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<br>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. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Metabolic difference detection | Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found. | From January 01, 2021 to December 31, 2021 | 
| Predictive model establishment | 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. | From January 01, 2022 to December 31, 2022 | 
| Predictive model validation | Verify the stability and accuracy of our model in larger cohorts and promote clinical translation. | From January 01, 2023 to December 31, 2023 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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breast cancer, sentinel lymph node metastasis, metabolic reprogramming, artificial intelligence