<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/NCT03237130</url>
  </required_header>
  <id_info>
    <org_study_id>CTRadiomics-LMesophagus-BNSF</org_study_id>
    <nct_id>NCT03237130</nct_id>
  </id_info>
  <brief_title>Prediction of Lymphatic Metastasis in Esophageal Cancer by CT Radiomics</brief_title>
  <official_title>Prediction of Lymphatic Metastasis in Esophageal Cancer by CT Radiomics</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Peking University Cancer Hospital &amp; Institute</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Peking University Cancer Hospital &amp; Institute</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      This study proposes to establish a CT radiomics-based prediction model for identifying&#xD;
      metastasis lymph nodes in esophageal cancer.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      This study proposes to establish an image feature extraction and selection method for&#xD;
      identifying lymph node metastasis of esophageal cancer.The study will consider the&#xD;
      relationship between the main lesion and the suspected lymph node, extract association&#xD;
      characteristics in order to improve the sensitivity and specificity of the CT radiomics-based&#xD;
      prediction model for identifying metastasis lymph nodes. The study includes the construction&#xD;
      of CT radiomics-based prediction model and the validation of the prediction model.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">February 15, 2017</start_date>
  <completion_date type="Actual">May 15, 2020</completion_date>
  <primary_completion_date type="Actual">November 7, 2019</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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>pathological local lymph node status</measure>
    <time_frame>Pathologic evaluation will be performed for each patient within 1 week after surgery</time_frame>
    <description>pathological lymph node status is defined as lymph node metastasis exist or not</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">133</enrollment>
  <condition>Esophagus Cancer</condition>
  <arm_group>
    <arm_group_label>preoperative enhanced chest CT</arm_group_label>
    <description>The cohort contains patients with preoperative therapies and patients without preoperative therapies. Each patient receives regular preoperative enhanced chest CT; for patients with preoperative therapies, they usually undergo twice enhanced chest CT examination at before and after preoperative therapies, the CT images after preoperative therapies will be used for analysis</description>
  </arm_group>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>preoperative enhanced chest CT examination</intervention_name>
    <description>Each patient will receive preoperative enhanced chest CT examination, and their CT images will be used for analysis</description>
    <arm_group_label>preoperative enhanced chest CT</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        pathologically proven esophageal cancer patients who receive preoperative CT and surgical&#xD;
        resection in our hospital.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  pathologically proven esophageal cancer&#xD;
&#xD;
          -  scheduled to preoperative enhanced chest CT and undergo surgery&#xD;
&#xD;
          -  sign the patient informed consent&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  failed to receive preoperative enhanced chest CT or undergo surgery&#xD;
&#xD;
          -  inavailable pathological results for local lymph node status&#xD;
&#xD;
          -  inquality of CT images for feature extraction&#xD;
&#xD;
          -  patient quit&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Ying-Shi Sun, MD</last_name>
    <role>Study Chair</role>
    <affiliation>Dept.Radiology,Peking University Cancer Hospital</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Beijing Cancer Hospital</name>
      <address>
        <city>Beijing</city>
        <state>Beijing</state>
        <zip>100142</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>January 2021</verification_date>
  <study_first_submitted>July 31, 2017</study_first_submitted>
  <study_first_submitted_qc>July 31, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 2, 2017</study_first_posted>
  <last_update_submitted>January 31, 2021</last_update_submitted>
  <last_update_submitted_qc>January 31, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">February 2, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Peking University Cancer Hospital &amp; Institute</investigator_affiliation>
    <investigator_full_name>Sun Ying-Shi</investigator_full_name>
    <investigator_title>Chairman of Dept.Radiology of Beijing Cancer Hospital</investigator_title>
  </responsible_party>
  <keyword>esophagus cancer; radiomics; computed tomograpy</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Esophageal Neoplasms</mesh_term>
    <mesh_term>Lymphatic Metastasis</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT03237130
org study id: CTRadiomics-LMesophagus-BNSF
nct id: NCT03237130
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
This study proposes to establish a CT radiomics-based prediction model for identifying
      metastasis lymph nodes in esophageal cancer.
This study proposes to establish an image feature extraction and selection method for
      identifying lymph node metastasis of esophageal cancer.The study will consider the
      relationship between the main lesion and the suspected lymph node, extract association
      characteristics in order to improve the sensitivity and specificity of the CT radiomics-based
      prediction model for identifying metastasis lymph nodes. The study includes the construction
      of CT radiomics-based prediction model and the validation of the prediction model.
observational model: Cohort
time perspective: Prospective
measure: pathological local lymph node status
time frame: Pathologic evaluation will be performed for each patient within 1 week after surgery
description: pathological lymph node status is defined as lymph node metastasis exist or not
arm group label: preoperative enhanced chest CT
description: The cohort contains patients with preoperative therapies and patients without preoperative therapies. Each patient receives regular preoperative enhanced chest CT; for patients with preoperative therapies, they usually undergo twice enhanced chest CT examination at before and after preoperative therapies, the CT images after preoperative therapies will be used for analysis
intervention type: Procedure
intervention name: preoperative enhanced chest CT examination
description: Each patient will receive preoperative enhanced chest CT examination, and their CT images will be used for analysis
arm group label: preoperative enhanced chest CT
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Ying-Shi Sun, MD
role: Study Chair
affiliation: Dept.Radiology,Peking University Cancer Hospital
facility: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Peking University Cancer Hospital & Institute
investigator full name: Sun Ying-Shi
investigator title: Chairman of Dept.Radiology of Beijing Cancer Hospital
mesh term: Esophageal Neoplasms
mesh term: Lymphatic Metastasis
sharing ipd: Undecided