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/NCT06025305</url>
    </required_header>
    <id_info>
      <org_study_id>2023DZKY-058-01</org_study_id>
      <nct_id>NCT06025305</nct_id>
    </id_info>
    <brief_title>Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography</brief_title>
    <acronym>VALUE</acronym>
    <official_title>Development and Validation of Multi-scale Deep Neural Network-Based CT Intelligent Diagnosis System for Coronary Vulnerable Plaques: A Chinese Multicenter Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Jinling Hospital, China</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Jinling Hospital, China</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>
        The goal of this observational study is to develop an automatic whole-process AI model to&#xD;
        detect, quantify, and characterize plaques using coronary CT angiography in coronary artery&#xD;
        disease patients. The main questions it aims to answer are:&#xD;
  &#xD;
          1. whether the AI model enables to detect, quantify coronary plaques compared with&#xD;
             intravascular ultrasound or expert readers;&#xD;
  &#xD;
          2. whether the AI model enables to identify vulnerable plaques using intravascular&#xD;
             ultrasound or optical coherence tomography as the referece standard.&#xD;
  &#xD;
          3. whether the AI model enables to predict future adverse cardiac events in a large cohort&#xD;
             of 10,000 patients with non-obstructive CAD.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Coronary artery disease (CAD) remains the leading cause of death worldwide. Atherosclerotic&#xD;
        plaques play a pivotal role in CAD-related patient mortality. Thus, the detection,&#xD;
        quantification, and characterization of coronary plaques are clinically significant for early&#xD;
        prevention and interventions for CAD.&#xD;
  &#xD;
        Coronary CT angiography (CCTA) has emerged as a robust noninvasive tool for the evaluation of&#xD;
        CAD. In clinical practice, the coronary plaque assessment is performed by a time-consuming&#xD;
        manual process dependent on the clinician's experience and subjective visual interpretation.&#xD;
        With the development of artificial intelligence, many automatic computer-aided methods have&#xD;
        been proposed to post-process the CCTA images. However, previous proposed algorithms of&#xD;
        plaque evaluation were not developed based on intravascular ultrasound (IVUS) or optical&#xD;
        coherence tomography (OCT), which were regarded as the gold reference for plaque evaluation.&#xD;
        Thus, we aimed to developed a deep learning model in a whole-process automatic and&#xD;
        intelligent system on CCTA to detect, quantify and characterize plaques using IVUS or OCT as&#xD;
        reference standard. Then we will work on the validation in different clinical scenarios: (i)&#xD;
        Validation of the accuracy of the new deep learning model; (ii) Prognosis of the model in&#xD;
        different populations with CAD.&#xD;
  &#xD;
        The main questions it aims to answer are:&#xD;
  &#xD;
          1. whether the AI model enables to detect, quantify coronary plaques compared with&#xD;
             intravascular ultrasound or expert readers;&#xD;
  &#xD;
          2. whether the AI model enables to identify vulnerable plaques using IVUS or OCT as the&#xD;
             reference standard.&#xD;
  &#xD;
          3. whether the AI model enables to predict future adverse cardiac events in a large cohort&#xD;
             of 10,000 patients with non-obstructive coronary artery disease (China CT-FFR study 2).&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Enrolling by invitation</overall_status>
    <start_date type="Actual">July 1, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2025</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2024</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>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging</measure>
      <time_frame>1 day</time_frame>
    </primary_outcome>
    <secondary_outcome>
      <measure>Overall coronary plaque detection rate using intravascular ultrasound as reference standard</measure>
      <time_frame>1 day</time_frame>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Total plaque volume</measure>
      <time_frame>1 day</time_frame>
    </secondary_outcome>
    <secondary_outcome>
      <measure>minimum lumen area measurement compared to intravascular ultrasound</measure>
      <time_frame>1 day</time_frame>
    </secondary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">2000</enrollment>
    <condition>Coronary Artery Disease</condition>
    <condition>Plaque, Atherosclerotic</condition>
    <arm_group>
      <arm_group_label>Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months</arm_group_label>
    </arm_group>
    <arm_group>
      <arm_group_label>Patients who underwent coronary CT angiography and optical coherence tomography within 3 months</arm_group_label>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Intravascular imaging test</intervention_name>
      <description>Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months</description>
      <arm_group_label>Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months</arm_group_label>
      <arm_group_label>Patients who underwent coronary CT angiography and optical coherence tomography within 3 months</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          consecutive patients who first underwent CCTA and then Intravascular imaging in China&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Intravascular imaging (including intravascular ultrasound or optical coherence&#xD;
               tomography) was performed within 3 months after CCTA;&#xD;
  &#xD;
            -  No change in medications or clinical symptoms during CCTA and intravascular imaging&#xD;
               examinations;&#xD;
  &#xD;
            -  Coronary artery diameter stenosis of 30% to 90% on invasive coronary imaging.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Image quality of CCTA or intravascular US was inadequate to analyze;&#xD;
  &#xD;
            -  Intravascular imaging was performed after percutaneous coronary intervention (PCI) or&#xD;
               pre-dilation of the target lesions;&#xD;
  &#xD;
            -  Lesions could not be co-registered between CCTA and intravascular US;&#xD;
  &#xD;
            -  Missing CCTA or intravascular US data&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Longjiang Zhang, MD</last_name>
      <role>Study Chair</role>
      <affiliation>Jinling Hospital, Medical School of Nanjing University, Nanjing,China</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>Research Institute Of Medical Imaging Jinling Hospital</name>
        <address>
          <city>Nanjing</city>
          <state>Jiangsu</state>
          <zip>210018</zip>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <reference>
      <citation>Follmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vazquez Mezquita AJ, Biavati F, Isgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2023 Jul 18. doi: 10.1038/s41569-023-00900-3. Online ahead of print.</citation>
      <PMID>37464183</PMID>
    </reference>
    <reference>
      <citation>Gaba P, Gersh BJ, Muller J, Narula J, Stone GW. Evolving concepts of the vulnerable atherosclerotic plaque and the vulnerable patient: implications for patient care and future research. Nat Rev Cardiol. 2023 Mar;20(3):181-196. doi: 10.1038/s41569-022-00769-8. Epub 2022 Sep 23.</citation>
      <PMID>36151312</PMID>
    </reference>
    <results_reference>
      <citation>Zhou F, Chen Q, Luo X, Cao W, Li Z, Zhang B, Schoepf UJ, Gill CE, Guo L, Gao H, Li Q, Shi Y, Tang T, Liu X, Wu H, Wang D, Xu F, Jin D, Huang S, Li H, Pan C, Gu H, Xie L, Wang X, Ye J, Jiang J, Zhao H, Fang X, Xu Y, Xing W, Li X, Yin X, Lu GM, Zhang LJ. Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study. Front Cardiovasc Med. 2022 Jan 31;8:778010. doi: 10.3389/fcvm.2021.778010. eCollection 2021.</citation>
      <PMID>35174219</PMID>
    </results_reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 30, 2023</study_first_submitted>
    <study_first_submitted_qc>September 5, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 6, 2023</study_first_posted>
    <last_update_submitted>September 5, 2023</last_update_submitted>
    <last_update_submitted_qc>September 5, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 6, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Jinling Hospital, China</investigator_affiliation>
      <investigator_full_name>Zhang longjiang,MD</investigator_full_name>
      <investigator_title>MD, PhD</investigator_title>
    </responsible_party>
    <keyword>artificial intelligence; coronary CT angiography; vulnerable plaque</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Coronary Artery Disease</mesh_term>
      <mesh_term>Plaque, Atherosclerotic</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- 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/NCT06025305
  org study id: 2023DZKY-058-01
  nct id: NCT06025305
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this observational study is to develop an automatic whole-process AI model to
        detect, quantify, and characterize plaques using coronary CT angiography in coronary artery
        disease patients. The main questions it aims to answer are:
  
          1. whether the AI model enables to detect, quantify coronary plaques compared with
             intravascular ultrasound or expert readers;
  
          2. whether the AI model enables to identify vulnerable plaques using intravascular
             ultrasound or optical coherence tomography as the referece standard.
  
          3. whether the AI model enables to predict future adverse cardiac events in a large cohort
             of 10,000 patients with non-obstructive CAD.
  Coronary artery disease (CAD) remains the leading cause of death worldwide. Atherosclerotic
        plaques play a pivotal role in CAD-related patient mortality. Thus, the detection,
        quantification, and characterization of coronary plaques are clinically significant for early
        prevention and interventions for CAD.
  
        Coronary CT angiography (CCTA) has emerged as a robust noninvasive tool for the evaluation of
        CAD. In clinical practice, the coronary plaque assessment is performed by a time-consuming
        manual process dependent on the clinician's experience and subjective visual interpretation.
        With the development of artificial intelligence, many automatic computer-aided methods have
        been proposed to post-process the CCTA images. However, previous proposed algorithms of
        plaque evaluation were not developed based on intravascular ultrasound (IVUS) or optical
        coherence tomography (OCT), which were regarded as the gold reference for plaque evaluation.
        Thus, we aimed to developed a deep learning model in a whole-process automatic and
        intelligent system on CCTA to detect, quantify and characterize plaques using IVUS or OCT as
        reference standard. Then we will work on the validation in different clinical scenarios: (i)
        Validation of the accuracy of the new deep learning model; (ii) Prognosis of the model in
        different populations with CAD.
  
        The main questions it aims to answer are:
  
          1. whether the AI model enables to detect, quantify coronary plaques compared with
             intravascular ultrasound or expert readers;
  
          2. whether the AI model enables to identify vulnerable plaques using IVUS or OCT as the
             reference standard.
  
          3. whether the AI model enables to predict future adverse cardiac events in a large cohort
             of 10,000 patients with non-obstructive coronary artery disease (China CT-FFR study 2).
  observational model: Cohort
  time perspective: Retrospective
  measure: Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging
  time frame: 1 day
  measure: Overall coronary plaque detection rate using intravascular ultrasound as reference standard
  time frame: 1 day
  measure: Total plaque volume
  time frame: 1 day
  measure: minimum lumen area measurement compared to intravascular ultrasound
  time frame: 1 day
  arm group label: Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months
  arm group label: Patients who underwent coronary CT angiography and optical coherence tomography within 3 months
  intervention type: Diagnostic Test
  intervention name: Intravascular imaging test
  description: Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months
  arm group label: Patients who underwent coronary CT angiography and intravascular ultrasound within 3 months
  arm group label: Patients who underwent coronary CT angiography and optical coherence tomography within 3 months
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Longjiang Zhang, MD
  role: Study Chair
  affiliation: Jinling Hospital, Medical School of Nanjing University, Nanjing,China
  facility: 
  country: China
  citation: Follmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vazquez Mezquita AJ, Biavati F, Isgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2023 Jul 18. doi: 10.1038/s41569-023-00900-3. Online ahead of print.
  PMID: 37464183
  citation: Gaba P, Gersh BJ, Muller J, Narula J, Stone GW. Evolving concepts of the vulnerable atherosclerotic plaque and the vulnerable patient: implications for patient care and future research. Nat Rev Cardiol. 2023 Mar;20(3):181-196. doi: 10.1038/s41569-022-00769-8. Epub 2022 Sep 23.
  PMID: 36151312
  citation: Zhou F, Chen Q, Luo X, Cao W, Li Z, Zhang B, Schoepf UJ, Gill CE, Guo L, Gao H, Li Q, Shi Y, Tang T, Liu X, Wu H, Wang D, Xu F, Jin D, Huang S, Li H, Pan C, Gu H, Xie L, Wang X, Ye J, Jiang J, Zhao H, Fang X, Xu Y, Xing W, Li X, Yin X, Lu GM, Zhang LJ. Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study. Front Cardiovasc Med. 2022 Jan 31;8:778010. doi: 10.3389/fcvm.2021.778010. eCollection 2021.
  PMID: 35174219
  responsible party type: Principal Investigator
  investigator affiliation: Jinling Hospital, China
  investigator full name: Zhang longjiang,MD
  investigator title: MD, PhD
  mesh term: Coronary Artery Disease
  mesh term: Plaque, Atherosclerotic
  sharing ipd: No