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Trial Title:
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
NCT ID:
NCT06001528
Condition:
Breast Cancer
Lymph Node Metastasis
Conditions: Official terms:
Breast Neoplasms
Neoplasm Metastasis
Lymphatic Metastasis
Conditions: Keywords:
breast cancer
sentinel lymph node metastasis
metabolic reprogramming
artificial intelligence
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Other
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, 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.
Criteria for eligibility:
Study pop:
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.
Sampling method:
Non-Probability Sample
Criteria:
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
Gender:
Female
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Shantou Central Hospital
Address:
City:
Shantou
Country:
China
Status:
Recruiting
Contact:
Last name:
Xiaorong Lin, Dr.
Phone:
13790891600
Email:
clarelynn_lin@163.com
Start date:
January 1, 2021
Completion date:
August 31, 2026
Lead sponsor:
Agency:
Shantou Central Hospital
Agency class:
Other
Collaborator:
Agency:
Zhejiang Cancer Hospital
Agency class:
Other
Collaborator:
Agency:
Sichuan Cancer Hospital and Research Institute
Agency class:
Other
Collaborator:
Agency:
Shenshan Medical Center of Sun Yat-sen Memorial Hospital
Agency class:
Other
Collaborator:
Agency:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class:
Other
Source:
Shantou Central Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06001528
