<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/NCT03795181</url>
  </required_header>
  <id_info>
    <org_study_id>PTHO1902</org_study_id>
    <nct_id>NCT03795181</nct_id>
  </id_info>
  <brief_title>Prediction Model for Multiple Pulmonary Nodules</brief_title>
  <official_title>Prediction Model for Multiple Pulmonary Nodules</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Peking University People's Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Peking University People's 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>
      This study compares the sensitivity, specificity and accuracy of radiologists, thoracic&#xD;
      surgeons and a predictive model (PKUM model) to discriminate malignancy from benign nodules&#xD;
      in patients with multiple pulmonary nodules.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      It is clinically difficult to diagnose and manage patients with multiple pulmonary nodules&#xD;
      (MPNs). We have developed a web-based mathematic model (PKUM model) by using a multi-centric&#xD;
      database from three institutions (Peking University People's Hospital, Haidian Section of&#xD;
      Peking University Third Hospital, People's Hospital Affiliated to Hebei Medical University)&#xD;
      to predict the probability of a nodule to be malignant in patients with MPNs. This&#xD;
      prospectively observational study will recruit patients with MPNs between January 2019 and&#xD;
      March 2019, allowing radiologists, surgeons, and a predictive model (PKUM model) to&#xD;
      discriminate malignancy from benign nodules, and compare their sensitivity, specificity, and&#xD;
      accuracy.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 1, 2019</start_date>
  <completion_date type="Actual">March 30, 2019</completion_date>
  <primary_completion_date type="Actual">March 30, 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>Performance of PKUM model</measure>
    <time_frame>3 months</time_frame>
    <description>Area under receiver operating characteristic curve (AUC) of PKUM model in predicting the probability of a nodule to be malignant in patients with multiple pulmonary nodules.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Comparison between PKUM model and clinicians</measure>
    <time_frame>3 months</time_frame>
    <description>Comparison of sensitivity and specificity of radiologists, thoracic surgeons, and PKUM model in predicting the probability of a nodule to be malignant in patients with multiple pulmonary nodules.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Performance of PKUM model in equivocal nodules which is difficult to judge by clinicians</measure>
    <time_frame>3 months</time_frame>
    <description>Sensitivity and specificity of PKUM model in predicting malignant probability of equivocal nodules judged by radiologists and thoracic surgeons.</description>
  </secondary_outcome>
  <enrollment type="Actual">59</enrollment>
  <condition>Multiple Pulmonary Nodules</condition>
  <eligibility>
    <study_pop>
      <textblock>
        The population include patients with multiple pulmonary nodules who will be diagnosed and&#xD;
        treated between January 1，2019 and March 30, 2019. Patients with at least 2 nodules&#xD;
        resected will be enrolled in this study.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients with newly discovered, 4-30 mm multiple pulmonary nodules shown on thoracic&#xD;
             CT scans&#xD;
&#xD;
          -  Patients with at least two nodules resected for pathological evaluation&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  History of malignancy within 5 years&#xD;
&#xD;
          -  Presence of pneumonia or pleural effusion on thoracic CT scans&#xD;
&#xD;
          -  Patients with none or only one nodule resected&#xD;
&#xD;
          -  Patients with initial chemo-radiation therapy&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>90 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Jun Wang</last_name>
    <role>Principal Investigator</role>
    <affiliation>Peking University People's Hospital</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Yuqing Huang</last_name>
    <role>Study Director</role>
    <affiliation>Haidian Section of Peking University Third Hospital</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Jiabao Liu</last_name>
    <role>Study Director</role>
    <affiliation>People's Hospital Affiliated to Hebei Medical University</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Yingtai Chen</last_name>
    <role>Study Director</role>
    <affiliation>Beijing Aerospace 711 Hospital</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Mingru Li</last_name>
    <role>Study Director</role>
    <affiliation>Beijing Aerospace 731 Hospital</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Peking University People's Hospital</name>
      <address>
        <city>Beijing</city>
        <zip>100044</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>August 2019</verification_date>
  <study_first_submitted>January 1, 2019</study_first_submitted>
  <study_first_submitted_qc>January 3, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 7, 2019</study_first_posted>
  <last_update_submitted>August 26, 2019</last_update_submitted>
  <last_update_submitted_qc>August 26, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">August 28, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Peking University People's Hospital</investigator_affiliation>
    <investigator_full_name>Jun Wang</investigator_full_name>
    <investigator_title>Chief，Thoracic Surgery Service</investigator_title>
  </responsible_party>
  <keyword>multiple pulmonary nodules; predictive model</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Multiple Pulmonary Nodules</mesh_term>
  </condition_browse>
  <!-- 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/NCT03795181
org study id: PTHO1902
nct id: NCT03795181
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
This study compares the sensitivity, specificity and accuracy of radiologists, thoracic
      surgeons and a predictive model (PKUM model) to discriminate malignancy from benign nodules
      in patients with multiple pulmonary nodules.
It is clinically difficult to diagnose and manage patients with multiple pulmonary nodules
      (MPNs). We have developed a web-based mathematic model (PKUM model) by using a multi-centric
      database from three institutions (Peking University People's Hospital, Haidian Section of
      Peking University Third Hospital, People's Hospital Affiliated to Hebei Medical University)
      to predict the probability of a nodule to be malignant in patients with MPNs. This
      prospectively observational study will recruit patients with MPNs between January 2019 and
      March 2019, allowing radiologists, surgeons, and a predictive model (PKUM model) to
      discriminate malignancy from benign nodules, and compare their sensitivity, specificity, and
      accuracy.
observational model: Cohort
time perspective: Prospective
measure: Performance of PKUM model
time frame: 3 months
description: Area under receiver operating characteristic curve (AUC) of PKUM model in predicting the probability of a nodule to be malignant in patients with multiple pulmonary nodules.
measure: Comparison between PKUM model and clinicians
time frame: 3 months
description: Comparison of sensitivity and specificity of radiologists, thoracic surgeons, and PKUM model in predicting the probability of a nodule to be malignant in patients with multiple pulmonary nodules.
measure: Performance of PKUM model in equivocal nodules which is difficult to judge by clinicians
time frame: 3 months
description: Sensitivity and specificity of PKUM model in predicting malignant probability of equivocal nodules judged by radiologists and thoracic surgeons.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 90 Years
healthy volunteers: No
last name: Jun Wang
role: Principal Investigator
affiliation: Peking University People's Hospital
last name: Yuqing Huang
role: Study Director
affiliation: Haidian Section of Peking University Third Hospital
last name: Jiabao Liu
role: Study Director
affiliation: People's Hospital Affiliated to Hebei Medical University
last name: Yingtai Chen
role: Study Director
affiliation: Beijing Aerospace 711 Hospital
last name: Mingru Li
role: Study Director
affiliation: Beijing Aerospace 731 Hospital
facility: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Peking University People's Hospital
investigator full name: Jun Wang
investigator title: Chief，Thoracic Surgery Service
mesh term: Multiple Pulmonary Nodules