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

Breast Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06001528
• Other study ID #: XR Lin
• Status: Recruiting
• Start date: January 1, 2021
• Est. completion date: August 31, 2026

Study information

• Verified date: September 2023
• Source: Shantou Central Hospital
• Contact: Xiaorong Lin, Dr.
• Phone: 13790891600
• Email: clarelynn_lin[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2400
• Est. completion date: August 31, 2026
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms
• Lymph Node Metastasis
• Lymphatic Metastasis
• Neoplasm Metastasis

Locations

Country	Name	City	State
China	Shantou Central Hospital	Shantou	Guangdong

Sponsors (5)

Lead Sponsor	Collaborator
Shantou Central Hospital	Shenshan Medical Center of Sun Yat-sen Memorial Hospital, Sichuan Cancer Hospital and Research Institute, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Zhejiang Cancer Hospital

Country where clinical trial is conducted

China, 

References & Publications (8)

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Chayakulkheeree J, Pungrassami D, Prueksadee J. Performance of breast magnetic resonance imaging in axillary nodal staging in newly diagnosed breast cancer patients. Pol J Radiol. 2019 Oct 18;84:e413-e418. doi: 10.5114/pjr.2019.89690. eCollection 2019. — View Citation

Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmacherther Elektrophysiol. 2022 Mar;33(1):34-41. doi: 10.1007/s00399-022-00839-x. Epub 2022 Feb 11. — View Citation

Richard V, Davey MG, Annuk H, Miller N, Kerin MJ. The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer. Mol Cancer. 2022 Apr 4;21(1):95. doi: 10.1186/s12943-022-01506-y. — View Citation

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. — View Citation

Xu Y, Su GH, Ma D, Xiao Y, Shao ZM, Jiang YZ. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther. 2021 Aug 20;6(1):312. doi: 10.1038/s41392-021-00729-7. — View Citation

Zhou H, Zhu L, Song J, Wang G, Li P, Li W, Luo P, Sun X, Wu J, Liu Y, Zhu S, Zhang Y. Liquid biopsy at the frontier of detection, prognosis and progression monitoring in colorectal cancer. Mol Cancer. 2022 Mar 25;21(1):86. doi: 10.1186/s12943-022-01556-2. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	From January 01, 2021 to December 31, 2021
Primary	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.	From January 01, 2022 to December 31, 2022
Primary	Predictive model validation	Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.	From January 01, 2023 to December 31, 2023