Clinical trial NCT06001528: Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
Official title: Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Breast Cancer
Lymph Node Metastasis
Keywords: breast cancer
sentinel lymph node metastasis
metabolic reprogramming
artificial intelligence
Lead sponsor: Shantou Central Hospital
Sponsor class: OTHER
Collaborators: name: Zhejiang Cancer Hospital
name: Sichuan Cancer Hospital and Research Institute
name: Shenshan Medical Center of Sun Yat-sen Memorial Hospital
name: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
First posted: 2023-08-21
Enrollment: 2400
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
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.

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.


Eligibility criteria: 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

Primary outcomes:
measure: Metabolic difference detection
timeFrame: From January 01, 2021 to December 31, 2021
measure: Predictive model establishment
timeFrame: From January 01, 2022 to December 31, 2022
measure: Predictive model validation
timeFrame: From January 01, 2023 to December 31, 2023
Secondary outcomes:
Other outcomes:
Locations:
city: Shantou
country: China
facility: Shantou Central Hospital
state: Guangdong
Study officials:
affiliation: Shantou Central Hospital
name: Xiaorong Lin, Dr.
role: STUDY_DIRECTOR
affiliation: Zhejiang Cancer Hospital
name: Hai Hu, Pro.
role: PRINCIPAL_INVESTIGATOR
affiliation: Shantou Central Hospital
name: Zhiyong Wu, Dr.
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
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.
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.
citation: 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.
citation: 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.
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.
citation: 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.