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

September 26, 2023 updated by: Shantou Central Hospital
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.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

2400

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Shantou, Guangdong, China
        • Recruiting
        • Shantou Central Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Retrospective cohort: The study retrospectively collected data from 724 patients with early breast cancer.

Prospective cohort: We expected the accuracy of our predictive model reached 96%, and given an estimated dropout rate of 10%. We needed to include at least 586 breast cancer in the prospective cohort. Therefore, we plan to prospectively enroll serum samples from 586 breast cancer patients to detect the abundance of metabolites and collect the radiological and pathological information from these patients for the following analysis.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Metabolic difference detection
Time Frame: 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.
From January 01, 2021 to December 31, 2021
Predictive model establishment
Time Frame: 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.
From January 01, 2022 to December 31, 2022
Predictive model validation
Time Frame: From January 01, 2023 to December 31, 2023
Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.
From January 01, 2023 to December 31, 2023

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shantou Central Hospital

Collaborators

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

Investigators

  • Study Director: Xiaorong Lin, Dr., Shantou Central Hospital
  • Principal Investigator: Hai Hu, Pro., Zhejiang Cancer Hospital
  • Principal Investigator: Zhiyong Wu, Dr., Shantou Central Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2021

Primary Completion (Estimated)

December 31, 2023

Study Completion (Estimated)

August 31, 2026

Study Registration Dates

First Submitted

August 14, 2023

First Submitted That Met QC Criteria

August 14, 2023

First Posted (Actual)

August 21, 2023

Study Record Updates

Last Update Posted (Actual)

September 28, 2023

Last Update Submitted That Met QC Criteria

September 26, 2023

Last Verified

September 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • XR Lin

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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