An observational study in Breast Cancer and Lymph Node Metastasis, sponsored by Shantou Central Hospital. Status unknown at 1 site in China. Open to female participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2023-09-28.
Sponsored by Shantou Central Hospital · Observational
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.
12,815 studies on the registry are indexed under Breast Neoplasms; 2,920 are open to participants now.
This study's planned enrollment of 2,400 is above the median of 184 across 2,711 observational studies indexed under Breast Neoplasms.
Browse Breast Neoplasms studies →Shantou Central Hospital is the lead sponsor of 4 studies on the registry; 2 are open to participants now.
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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.
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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 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 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.
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.
Time frame: 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.
Time frame: 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.
Time frame: From January 01, 2023 to December 31, 2023
Started Jan 2021 (actual). Primary completion is estimated for Dec 2023, 35 months after the start.
Of 1,012 non-industry observational studies under Breast Neoplasms, half reached primary completion within 60 months of starting (the median), counting those still running; a quarter within 26 and three quarters within 141. This study's planned 35 months is below that.
Counting every sponsor, half of 1,165 studies reached it within 58 months; a quarter within 26 and three quarters within 125.
How these figures are worked out, and which studies are left out of them.
This study is status unknown, as verified in Sep 2023. You cannot join it, but the record below documents what was studied.
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Shantou Central Hospital