xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06001528</url>
    </required_header>
    <id_info>
      <org_study_id>XR Lin</org_study_id>
      <nct_id>NCT06001528</nct_id>
    </id_info>
    <brief_title>Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer</brief_title>
    <official_title>Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Shantou Central Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Zhejiang Cancer Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Sichuan Cancer Hospital and Research Institute</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Shenshan Medical Center of Sun Yat-sen Memorial Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Shantou Central Hospital</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Breast cancer is a malignant tumor with the highest morbidity and mortality among women&#xD;
        worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and&#xD;
        decisions regarding treatment modalities in breast cancer patient. Among patients who&#xD;
        underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological&#xD;
        results and in other words, these 70 % of the patients received unnecessary surgery. At&#xD;
        present, imaging and pathological diagnosis is the main measure of lymph node metastasis in&#xD;
        breast cancer. However, limitations remained. Artificial intelligence, including deep&#xD;
        learning and machine learning algorithms, has emerged as a possible technique, which can make&#xD;
        a more accuracy prediction through machine-based collection, learning and processing of&#xD;
        previous information, especially in radiology and pathology-based diagnosis. With the&#xD;
        intensification of the concept of precision medicine and the development of non-invasive&#xD;
        technology, we intend to use the artificial intelligence technology to develop a serum and&#xD;
        tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with&#xD;
        imaging and pathological information, providing specific, efficient and non-invasive&#xD;
        biological indicators for the monitoring and early intervention of lymph node metastasis in&#xD;
        patient with breast cancer. Therefore, we retrospectively include serum samples from early&#xD;
        breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort&#xD;
        and a modeling cohort. Metabolites were detected and screened in the discovery cohort and&#xD;
        then as the target metabolites for targeted detection in the modeling cohort. Combined with&#xD;
        preoperative imaging and pathological information, a prediction model of breast cancer&#xD;
        sentinel lymph node metastasis based on serum metabolites would be established. Subsequently,&#xD;
        multi-center breast cancer patients will prospectively include to verify the accuracy and&#xD;
        stability of our model.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">January 1, 2021</start_date>
    <completion_date type="Anticipated">August 31, 2026</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Other</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Metabolic difference detection</measure>
      <time_frame>From January 01, 2021 to December 31, 2021</time_frame>
      <description>Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Predictive model establishment</measure>
      <time_frame>From January 01, 2022 to December 31, 2022</time_frame>
      <description>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.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Predictive model validation</measure>
      <time_frame>From January 01, 2023 to December 31, 2023</time_frame>
      <description>Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.</description>
    </primary_outcome>
    <number_of_groups>3</number_of_groups>
    <enrollment type="Anticipated">2400</enrollment>
    <condition>Breast Cancer</condition>
    <condition>Lymph Node Metastasis</condition>
    <arm_group>
      <arm_group_label>Discovering cohort</arm_group_label>
      <description>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.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Modeling cohort</arm_group_label>
      <description>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.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Validation cohort</arm_group_label>
      <description>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.</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          Retrospective cohort: The study retrospectively collected data from 724 patients with early&#xD;
          breast cancer.&#xD;
  &#xD;
          Prospective cohort: We expected the accuracy of our predictive model reached 96%, and given&#xD;
          an estimated dropout rate of 10%. We needed to include at least 586 breast cancer in the&#xD;
          prospective cohort. Therefore, we plan to prospectively enroll serum samples from 586&#xD;
          breast cancer patients to detect the abundance of metabolites and collect the radiological&#xD;
          and pathological information from these patients for the following analysis.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria: patients with pathologically confirmed breast cancer; no preoperative&#xD;
          therapy including chemotherapy or endocrine therapy and no distant metastasis; underwent&#xD;
          mastectomy or breast-conserving surgery with sentinel lymph node biopsy; patients who&#xD;
          agreed to provide preoperative peripheral blood samples and had access to imaging,&#xD;
          pathological and follow-up data for preoperative and postoperative evaluation of the&#xD;
          disease.&#xD;
  &#xD;
          Exclusion Criteria:previous neoadjuvant therapy; presence of distant metastasis at time of&#xD;
          diagnosis; Primary malignancies other than breast cancer; bilateral breast cancer or&#xD;
          previous contralateral breast cancer; undergo modified radical surgery for breast cancer&#xD;
          without sentinel lymph node biopsy; cases with incomplete pathological data and follow-up&#xD;
          data; pregnant women; other patients determined by the investigator to be ineligible for&#xD;
          inclusion in the study.&#xD;
        </textblock>
      </criteria>
      <gender>Female</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Xiaorong Lin, Dr.</last_name>
      <role>Study Director</role>
      <affiliation>Shantou Central Hospital</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Hai Hu, Pro.</last_name>
      <role>Principal Investigator</role>
      <affiliation>Zhejiang Cancer Hospital</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Zhiyong Wu, Dr.</last_name>
      <role>Principal Investigator</role>
      <affiliation>Shantou Central Hospital</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Xiaorong Lin, Dr.</last_name>
      <phone>13790891600</phone>
      <email>clarelynn_lin@163.com</email>
    </overall_contact>
    <location>
      <facility>
        <name>Shantou Central Hospital</name>
        <address>
          <city>Shantou</city>
          <state>Guangdong</state>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Xiaorong Lin, Dr.</last_name>
        <phone>13790891600</phone>
        <email>clarelynn_lin@163.com</email>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <reference>
      <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>
      <PMID>33538338</PMID>
    </reference>
    <reference>
      <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>
      <PMID>34417437</PMID>
    </reference>
    <reference>
      <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>
      <PMID>35337361</PMID>
    </reference>
    <reference>
      <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>
      <PMID>35379239</PMID>
    </reference>
    <reference>
      <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>
      <PMID>31969959</PMID>
    </reference>
    <reference>
      <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>
      <PMID>30671931</PMID>
    </reference>
    <reference>
      <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>
      <PMID>35147766</PMID>
    </reference>
    <reference>
      <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.</citation>
      <PMID>30720861</PMID>
    </reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 14, 2023</study_first_submitted>
    <study_first_submitted_qc>August 14, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 21, 2023</study_first_posted>
    <last_update_submitted>August 14, 2023</last_update_submitted>
    <last_update_submitted_qc>August 14, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 21, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>breast cancer</keyword>
    <keyword>sentinel lymph node metastasis</keyword>
    <keyword>metabolic reprogramming</keyword>
    <keyword>artificial intelligence</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Breast Neoplasms</mesh_term>
      <mesh_term>Neoplasm Metastasis</mesh_term>
      <mesh_term>Lymphatic Metastasis</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06001528
  org study id: XR Lin
  nct id: NCT06001528
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  is fda regulated drug: No
  is fda regulated device: No
  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, we 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, we 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 include to verify the accuracy and
        stability of our model.
  observational model: Cohort
  time perspective: Other
  measure: Metabolic difference detection
  time frame: From January 01, 2021 to December 31, 2021
  description: Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found.
  measure: Predictive model establishment
  time frame: From January 01, 2022 to December 31, 2022
  description: 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.
  measure: Predictive model validation
  time frame: From January 01, 2023 to December 31, 2023
  description: Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.
  arm group label: Discovering cohort
  description: 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.
  arm group label: Modeling cohort
  description: 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.
  arm group label: Validation cohort
  description: 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.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: Female
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Xiaorong Lin, Dr.
  role: Study Director
  affiliation: Shantou Central Hospital
  last name: Hai Hu, Pro.
  role: Principal Investigator
  affiliation: Zhejiang Cancer Hospital
  last name: Zhiyong Wu, Dr.
  role: Principal Investigator
  affiliation: Shantou Central Hospital
  last name: Xiaorong Lin, Dr.
  phone: 13790891600
  email: clarelynn_lin@163.com
  facility: 
  status: Recruiting
  contact: 
  country: China
  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.
  PMID: 33538338
  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.
  PMID: 34417437
  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.
  PMID: 35337361
  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.
  PMID: 35379239
  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.
  PMID: 31969959
  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.
  PMID: 30671931
  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.
  PMID: 35147766
  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.
  PMID: 30720861
  responsible party type: Sponsor
  mesh term: Breast Neoplasms
  mesh term: Neoplasm Metastasis
  mesh term: Lymphatic Metastasis