Neoplasms

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Breast Neoplasms

Neoplasm Metastasis

Lymphatic Metastasis

Breast Diseases

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Neoplastic Processes

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.

Xiaorong Lin, Dr.

Hai Hu, Pro.

Zhiyong Wu, Dr.

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.

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

Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found.

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.

Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.

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

Completion axillary treatment according to the Dutch breast cancer guideline

Completion axillary treatment

Marjolein L Smidt, MD, PhD

Hans JW de Wilt, MD, PhD

STUDY AIM To decrease the number of breast cancer patients receiving overtreatment of the axilla, in order to positively influence the axillary morbidity rate and quality of life.

PRIMARY OBJECTIVE To determine whether omitting completion axillary treatment is not inferior to the current axillary treatment regimen in sentinel node positive breast cancer patients undergoing a mastectomy, in terms of regional recurrence rate.

HYPOTHESIS Completion axillary treatment can be safely omitted in sentinel node positive breast cancer patients undergoing a mastectomy. This will lead to a decreased axillary morbidity rate and to an increased quality of life, with non-inferior regional control, distant-disease free- and overall survival rates.

Omitting Completion Axillary Treatment in Sentinel Node Positive Breast Cancer Patients Undergoing a Mastectomy

The Value of Completion Axillary Treatment in Sentinel Node Positive Breast Cancer Patients Undergoing a Mastectomy. A Dutch Randomized Controlled Multicentre Trial.

Regional recurrence is defined as tumour recurrence and as residual tumour that became clinically apparent in ipsilateral axillary, infraclavicular and supraclavicular lymph nodes (pathologically proven).

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer	NCT06001528	Breast Cancer Lymph Node Meta...		18 Years -	Shantou Central Hospital
Omitting Completion Axillary Treatment in Sentinel Node Positive Breast Cancer Patients Undergoing a Mastectomy	NCT02112682	Breast Neoplasm...	Completion axil...	18 Years -	Maastricht University Medical Center

Spots Global Cancer Trial Database for sentinel lymph node metastasis