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

NCT06001528 | Recruiting

• 1 other identifier: XR Lin
• Study Type: observational
• Target: 2,400
• Locations: 1 country
• Sites: 1

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.

Conditions

Breast Cancer; Lymph Node Metastasis

Keywords

breast cancer; sentinel lymph node metastasis; metabolic reprogramming; artificial intelligence

Outcome Measures

Primary Outcomes (3)

• 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

• 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

• 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

Study Arms (3)

Discovering cohort

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

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

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Shantou Central Hospital: lead
• Zhejiang Cancer Hospital: collaborator
• Sichuan Cancer Hospital and Research Institute: collaborator
• Shenshan Medical Center of Sun Yat-sen Memorial Hospital: collaborator
• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: collaborator

Study Sites (1)

Shantou Central Hospital

Shantou, Guangdong, China

RECRUITING

Related Publications (8)

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  PMID: 33538338 BACKGROUND

• 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.

  PMID: 34417437 BACKGROUND

• 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.

  PMID: 35337361 BACKGROUND

• 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.

  PMID: 35379239 BACKGROUND

• 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.

  PMID: 31969959 BACKGROUND

• Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: A comprehensive review. Clin Genet. 2019 Jun;95(6):643-660. doi: 10.1111/cge.13514. Epub 2019 Feb 27.

  PMID: 30671931 BACKGROUND

• 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.

  PMID: 35147766 BACKGROUND

• Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5.

  PMID: 30720861 BACKGROUND

MeSH Terms

Conditions

Breast Neoplasms; Lymphatic Metastasis

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Neoplasm Metastasis; Neoplastic Processes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Xiaorong Lin, Dr.

  Shantou Central Hospital

  STUDY DIRECTOR

• Hai Hu, Pro.

  Zhejiang Cancer Hospital

  PRINCIPAL INVESTIGATOR

• Zhiyong Wu, Dr.

  Shantou Central Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiaorong Lin, Dr.

CONTACT

13790891600; clarelynn_lin@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 14, 2023
• First Posted: August 21, 2023
• Study Start: January 1, 2021
• Primary Completion: December 31, 2023
• Study Completion (Estimated): August 31, 2026
• Last Updated: September 28, 2023

Record last verified: 2023-09

Locations

CN (1)