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/NCT06019923</url>
    </required_header>
    <id_info>
      <org_study_id>IRB-20230265-R</org_study_id>
      <nct_id>NCT06019923</nct_id>
    </id_info>
    <brief_title>Study on the Dynamic Changes of Metabolic Biomarkers and Their Prognostic Relationship in Ovarian Cancer</brief_title>
    <official_title>Study on the Dynamic Changes of Metabolic Biomarkers and Their Prognostic Relationship in Ovarian Cancer</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Women's Hospital School Of Medicine Zhejiang University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Women's Hospital School Of Medicine Zhejiang University</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>
        Ovarian cancer is a highly lethal gynecological malignancy, often diagnosed at an advanced&#xD;
        stage, with high rates of recurrence within 1-2 years after frontline treatment. Current&#xD;
        guidelines recommend monitoring tumor markers CA125 and HE4 for disease progression, but&#xD;
        these markers may not detect recurrence or disease progression when their levels are below&#xD;
        the detection limit. Therefore, there is a need to identify new prognostic biomarkers and&#xD;
        monitor their dynamic changes for effective risk stratification and personalized treatment in&#xD;
        patients with ovarian cancer&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Ovarian Cancer is the deadliest gynecological malignancy, with over 70% of patients being&#xD;
        diagnosed at advanced stages, and more than 70% experiencing recurrence within 1-2 years&#xD;
        after frontline treatment. The recommended tumor biomarkers for monitoring ovarian cancer&#xD;
        progression, CA125 and HE4, still pose the risk of recurrence and disease progression when&#xD;
        their levels are below the detection limit. Therefore, it is of paramount importance to&#xD;
        search for new prognostic monitoring biomarkers for ovarian cancer in order to stratify the&#xD;
        prognosis and implement personalized treatment, ultimately improving patient outcomes.&#xD;
        Previous research and literature have indicated that metabolic biomarkers can directly&#xD;
        reflect the biochemical changes, physiological status, and disease progression in cancer&#xD;
        patients. In comparison to studying the relationship between metabolite expression levels at&#xD;
        a single time point and disease prognosis, the dynamic changes in metabolite trajectories&#xD;
        with multiple time points can better reflect the dynamic patterns of disease progression&#xD;
        throughout the entire cancer cycle, providing more prognostic information for patients with&#xD;
        ovarian cancer.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 10, 2023</start_date>
    <completion_date type="Anticipated">September 10, 2027</completion_date>
    <primary_completion_date type="Anticipated">September 10, 2026</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Progression-Free-Survival</measure>
      <time_frame>36 months</time_frame>
      <description>the length of time after surgical treatment for ovarian cancer that a patient lives without any signs or symptoms of the disease getting worse</description>
    </primary_outcome>
    <enrollment type="Anticipated">100</enrollment>
    <condition>Ovarian Cancer</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>No intervention</intervention_name>
      <description>No intervention</description>
    </intervention>
    <biospec_retention>Samples Without DNA</biospec_retention>
    <biospec_descr>
      <textblock>
        The collected plasma samples are used for plasma metabolomics analysis.&#xD;
      </textblock>
    </biospec_descr>
    <eligibility>
      <study_pop>
        <textblock>
          Women who were diagnosed as high-grade serous ovarian cancer&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age between 18 and 75 years;&#xD;
  &#xD;
            -  Pathological diagnosis of high-grade serous ovarian cancer;&#xD;
  &#xD;
            -  Newly diagnosed ovarian cancer case without prior neoadjuvant therapy.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Non-primary (recurrent) patients;&#xD;
  &#xD;
            -  Ovarian cancer patients who have not undergone surgical treatment;&#xD;
  &#xD;
            -  Patients with a history of other malignancies.&#xD;
        </textblock>
      </criteria>
      <gender>Female</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>75 Years</maximum_age>
    </eligibility>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 24, 2023</study_first_submitted>
    <study_first_submitted_qc>August 24, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 31, 2023</study_first_posted>
    <last_update_submitted>August 24, 2023</last_update_submitted>
    <last_update_submitted_qc>August 24, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 31, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Ovarian Neoplasms</mesh_term>
      <mesh_term>Carcinoma, Ovarian Epithelial</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/NCT06019923
  org study id: IRB-20230265-R
  nct id: NCT06019923
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  Ovarian cancer is a highly lethal gynecological malignancy, often diagnosed at an advanced
        stage, with high rates of recurrence within 1-2 years after frontline treatment. Current
        guidelines recommend monitoring tumor markers CA125 and HE4 for disease progression, but
        these markers may not detect recurrence or disease progression when their levels are below
        the detection limit. Therefore, there is a need to identify new prognostic biomarkers and
        monitor their dynamic changes for effective risk stratification and personalized treatment in
        patients with ovarian cancer
  Ovarian Cancer is the deadliest gynecological malignancy, with over 70% of patients being
        diagnosed at advanced stages, and more than 70% experiencing recurrence within 1-2 years
        after frontline treatment. The recommended tumor biomarkers for monitoring ovarian cancer
        progression, CA125 and HE4, still pose the risk of recurrence and disease progression when
        their levels are below the detection limit. Therefore, it is of paramount importance to
        search for new prognostic monitoring biomarkers for ovarian cancer in order to stratify the
        prognosis and implement personalized treatment, ultimately improving patient outcomes.
        Previous research and literature have indicated that metabolic biomarkers can directly
        reflect the biochemical changes, physiological status, and disease progression in cancer
        patients. In comparison to studying the relationship between metabolite expression levels at
        a single time point and disease prognosis, the dynamic changes in metabolite trajectories
        with multiple time points can better reflect the dynamic patterns of disease progression
        throughout the entire cancer cycle, providing more prognostic information for patients with
        ovarian cancer.
  observational model: Case-Only
  time perspective: Prospective
  measure: Progression-Free-Survival
  time frame: 36 months
  description: the length of time after surgical treatment for ovarian cancer that a patient lives without any signs or symptoms of the disease getting worse
  intervention type: Other
  intervention name: No intervention
  description: No intervention
  The collected plasma samples are used for plasma metabolomics analysis.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: Female
  minimum age: 18 Years
  maximum age: 75 Years
  responsible party type: Sponsor
  mesh term: Ovarian Neoplasms
  mesh term: Carcinoma, Ovarian Epithelial