Breast Cancer, Lymph Node Metastasis
Conditions
Keywords
breast cancer, sentinel lymph node metastasis, metabolic reprogramming, artificial intelligence
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, 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.
Interventions
None listed
Sponsors
Zhejiang Cancer Hospital
Sichuan Cancer Hospital and Research Institute
Shenshan Medical Center of Sun Yat-sen Memorial Hospital
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Shantou Central Hospital
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Contacts
Primary ContactXiaorong Lin, Dr.
Outcome results
None listed