<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/NCT04497233</url>
  </required_header>
  <id_info>
    <org_study_id>SHCHE202001</org_study_id>
    <nct_id>NCT04497233</nct_id>
  </id_info>
  <brief_title>Prediction Model of Peripheral Pulmonary Lesions Based on R-EBUS Image</brief_title>
  <acronym>R-EBUS</acronym>
  <official_title>Prediction Model of Benign and Malignant Peripheral Pulmonary Lesions Based on Image of Radial Endobronchial Ultrasound</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Shanghai Chest Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Shanghai Chest Hospital</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>
      Peripheral pulmonary lesions(PPLs) have a wide spectrum of diseases, and the diagnosis will&#xD;
      affect the treatment strategy and prognosis. Radial endobronchial ultrasound (R-EBUS) can be&#xD;
      used for non-invasive diagnosis of PPLs, and the supplement pathological diagnosis results of&#xD;
      EBUS-TBLB, which has important clinical application value. This project intends to select&#xD;
      representative images from R-EBUS dynamic videos for qualitative and quantitative analysis,&#xD;
      to establish and verify the diagnostic evaluation system of R-EBUS forPPLs. Then build 1,000&#xD;
      R-EBUS image databases of PPLs, train deep learning networks for automatic extraction and&#xD;
      diagnosis of target areas, and automatically extract representative images from videos to&#xD;
      establish a benign and malignant prediction model of PPLs. We will provide reliable&#xD;
      theoretical basis for the diagnosis of PPLs, and optimize the diagnosis and treatment&#xD;
      method.The network would be prospectively verified through 300 R-EBUS images from multi&#xD;
      centers.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      PPLs are lesions at tertiary bronchus and above. The lesion cannot be seen by conventional&#xD;
      bronchoscopy and the diagnosis will affect the treatment strategy and prognosis. R-EBUS can&#xD;
      be used for non-invasive diagnosis of PPLs, and the supplement pathological diagnosis results&#xD;
      of EBUS-TBLB. During the procedure, target PPLs are examined by ultrasound host (EU-ME2,&#xD;
      Olympus, Tokyo, Japan) equipped with Doppler function and ultrasound probe . The bronchoscope&#xD;
      reaches the distal as far as possible according to the predetermined position on chest CT or&#xD;
      positron emission tomography-computed tomography (PET-CT) . The R-EBUS probe is inserted into&#xD;
      the working channel of the bronchoscope, and gradually approaches the target PPL to obtain&#xD;
      R-EBUS image. According to the characteristics such as within or adjacent to image, the probe&#xD;
      scan the lesion from the near end to the far end and record the video. The recording time is&#xD;
      required longer than 10 seconds. After selecting a typical R-EBUS image, freeze the image and&#xD;
      take a screenshot. The long and short diameter of the lesion will be measured. This project&#xD;
      includes three parts: preliminary construction and evaluation of R-EBUS image system for&#xD;
      benign and malignant PPLs, construction of R-EBUS artificial intelligence prediction model&#xD;
      and multi-center prospective validation of the prediction model. A total of 1000 patients&#xD;
      will be enrolled to construct diagnostic model and 300 are enrolled to verify the diagnostic&#xD;
      effiency.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">July 1, 2018</start_date>
  <completion_date type="Anticipated">March 31, 2021</completion_date>
  <primary_completion_date type="Anticipated">September 30, 2020</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>Other</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Diagnostic efficacy of R-EBUS prediction model.</measure>
    <time_frame>18 months</time_frame>
    <description>Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Diagnostic efficacy of traditional qualitative and quantitative methods</measure>
    <time_frame>18 months</time_frame>
    <description>Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">1300</enrollment>
  <condition>Diagnoses Disease</condition>
  <arm_group>
    <arm_group_label>Prospectively validation group</arm_group_label>
    <description>Two diagnosis methods will be used in the prospective validation section, one is traditional qualitative and quantitative method, the other is artificial intelligence prediction model based on videos to compare the diagnostic efficacy.</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        The population of this study are those who meet the inclusion and exclusion criteria.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Patients with PPLs in Chest CT or PET / CT;&#xD;
&#xD;
          2. Chest CT or PET / CT shows PPLs with diameter greater than 8 mm;&#xD;
&#xD;
          3. Patients agree to perform EBUS-TBLB and sign the informed consent.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Thin-layer Chest CT or PET / CT indicates lack of bronchial access to PPLs;&#xD;
&#xD;
          2. Patients refuse to participate in this clinical trial;&#xD;
&#xD;
          3. Patients with severe cardiopulmonary dysfunction or other indications that not allowed&#xD;
             for bronchoscopy;&#xD;
&#xD;
          4. Visible lesions in the lumen during conventional bronchoscopy;&#xD;
&#xD;
          5. Patients have other related contraindications of bronchoscopy;&#xD;
&#xD;
          6. Patients have other reasons unfit for this study.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Jiayuan Sun, PhD</last_name>
    <role>Study Director</role>
    <affiliation>Shanghai Chest Hospital</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Jiayuan Sun, PhD</last_name>
    <phone>18017321598</phone>
    <email>jysun1976@163.com</email>
  </overall_contact>
  <location>
    <facility>
      <name>Shanghai Chest Hospital</name>
      <address>
        <city>Shanghai</city>
        <country>China</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Jiayuan Sun, PhD</last_name>
      <phone>18017321598</phone>
      <email>jysun1976@163.com</email>
    </contact>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 19, 2020</study_first_submitted>
  <study_first_submitted_qc>July 29, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">August 4, 2020</study_first_posted>
  <last_update_submitted>July 29, 2020</last_update_submitted>
  <last_update_submitted_qc>July 29, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">August 4, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Shanghai Chest Hospital</investigator_affiliation>
    <investigator_full_name>Jiayuan Sun</investigator_full_name>
    <investigator_title>Director,Endoscope Department,Shanghai Chest Hospital</investigator_title>
  </responsible_party>
  <keyword>R-EBUS image</keyword>
  <keyword>Deep learning</keyword>
  <keyword>Peripheral pulmonary lesions</keyword>
  <keyword>Prediction model</keyword>
  <keyword>Diagnosis</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Disease</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/NCT04497233
org study id: SHCHE202001
nct id: NCT04497233
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
Peripheral pulmonary lesions(PPLs) have a wide spectrum of diseases, and the diagnosis will
      affect the treatment strategy and prognosis. Radial endobronchial ultrasound (R-EBUS) can be
      used for non-invasive diagnosis of PPLs, and the supplement pathological diagnosis results of
      EBUS-TBLB, which has important clinical application value. This project intends to select
      representative images from R-EBUS dynamic videos for qualitative and quantitative analysis,
      to establish and verify the diagnostic evaluation system of R-EBUS forPPLs. Then build 1,000
      R-EBUS image databases of PPLs, train deep learning networks for automatic extraction and
      diagnosis of target areas, and automatically extract representative images from videos to
      establish a benign and malignant prediction model of PPLs. We will provide reliable
      theoretical basis for the diagnosis of PPLs, and optimize the diagnosis and treatment
      method.The network would be prospectively verified through 300 R-EBUS images from multi
      centers.
PPLs are lesions at tertiary bronchus and above. The lesion cannot be seen by conventional
      bronchoscopy and the diagnosis will affect the treatment strategy and prognosis. R-EBUS can
      be used for non-invasive diagnosis of PPLs, and the supplement pathological diagnosis results
      of EBUS-TBLB. During the procedure, target PPLs are examined by ultrasound host (EU-ME2,
      Olympus, Tokyo, Japan) equipped with Doppler function and ultrasound probe . The bronchoscope
      reaches the distal as far as possible according to the predetermined position on chest CT or
      positron emission tomography-computed tomography (PET-CT) . The R-EBUS probe is inserted into
      the working channel of the bronchoscope, and gradually approaches the target PPL to obtain
      R-EBUS image. According to the characteristics such as within or adjacent to image, the probe
      scan the lesion from the near end to the far end and record the video. The recording time is
      required longer than 10 seconds. After selecting a typical R-EBUS image, freeze the image and
      take a screenshot. The long and short diameter of the lesion will be measured. This project
      includes three parts: preliminary construction and evaluation of R-EBUS image system for
      benign and malignant PPLs, construction of R-EBUS artificial intelligence prediction model
      and multi-center prospective validation of the prediction model. A total of 1000 patients
      will be enrolled to construct diagnostic model and 300 are enrolled to verify the diagnostic
      effiency.
observational model: Case-Only
time perspective: Other
measure: Diagnostic efficacy of R-EBUS prediction model.
time frame: 18 months
description: Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy
measure: Diagnostic efficacy of traditional qualitative and quantitative methods
time frame: 18 months
description: Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy
arm group label: Prospectively validation group
description: Two diagnosis methods will be used in the prospective validation section, one is traditional qualitative and quantitative method, the other is artificial intelligence prediction model based on videos to compare the diagnostic efficacy.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
last name: Jiayuan Sun, PhD
role: Study Director
affiliation: Shanghai Chest Hospital
last name: Jiayuan Sun, PhD
phone: 18017321598
email: jysun1976@163.com
facility: 
status: Recruiting
contact: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Shanghai Chest Hospital
investigator full name: Jiayuan Sun
investigator title: Director,Endoscope Department,Shanghai Chest Hospital
mesh term: Disease
sharing ipd: Undecided