Diagnostic Biomarkers Exploration of Breast Cancer From Serum and Urine
Overview
- Phase
- N/A
- Intervention
- Not specified
- Conditions
- Breast Neoplasm Female
- Sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Enrollment
- 150
- Locations
- 1
- Primary Endpoint
- The different biomarkers in malignant and benign breast diseases
- Status
- Recruiting
- Last Updated
- 2 years ago
Overview
Brief Summary
The goal of this observational study is to find the diagnostic biomarkers in serum and urine from early breast cancer patients. The main questions it aims to answer are:
- compare the different biomarkers in serum and urine from breast cancer patients, benign lesions and healthy population.
- construct the best diagnostic model by machine learning to distinguish breast cancer and non-breast cancer patients.
Participants, including breast and non-breast cancer patients will be asked to provides blood and urine during their diagnosis and treatment process without changing the original treatment. When necessary, specimens will be collected during the surgery，without affecting pathological diagnosis.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Signing the consent of informedness;
- •Patients with breast mass who need surgery after examination;
- •Cardiac ultrasound indicates that the blood score of the heart is within the normal range;
- •ECOG≤0-2 points;
- •Oversure function is acceptable.
Exclusion Criteria
- •Merge other malignant tumors such as gynecologic oncology;
- •After evaluation, the internal organs are not suitable.
Outcomes
Primary Outcomes
The different biomarkers in malignant and benign breast diseases
Time Frame: It is expected to be one to two years.
By analyzing the differences (eg:PCA, FC and et al.)in the composition of proteins in blood and urine, biomarkers with significant differences between the two groups will be obtained.
Diagnostic models used different biomarkers by machine learning
Time Frame: Within half a year after the completion of the test.
Using biomarkers that detect discrepancies, combined with machine learning to build early breast cancer diagnostic models.