<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/NCT04482114</url>
  </required_header>
  <id_info>
    <org_study_id>PI2020_843_0052</org_study_id>
    <nct_id>NCT04482114</nct_id>
  </id_info>
  <brief_title>Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)</brief_title>
  <acronym>DLIRTHORAX</acronym>
  <official_title>Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Centre Hospitalier Universitaire, Amiens</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Centre Hospitalier Universitaire, Amiens</source>
  <oversight_info>
    <has_dmc>No</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>
      evaluate the diagnostic performance of ultra-low dose CT using deep learning-based&#xD;
      reconstruction in the detection of pulmonary nodules.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      -  Background: Lung cancer is the leading cause of cancer deaths. Patients with pulmonary&#xD;
           nodules often undergo multiple computed tomography (CT) examinations for diagnostic and&#xD;
           follow-up purposes.&#xD;
&#xD;
        -  Purpose: The main objective of this study is to evaluate the diagnostic performance of&#xD;
           ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary&#xD;
           nodules.&#xD;
&#xD;
        -  Abstract: Despite recent advances, lung cancer remains the most commonly diagnosed&#xD;
           cancer and the leading cause of cancer death worldwide because it is often diagnosed at&#xD;
           advanced stages that are not surgically curable. Nevertheless, early detection of lung&#xD;
           cancer allows surgical resection, offers curative treatment and the best chance of&#xD;
           survival. There is currently no screening program in France, but individual screening&#xD;
           can be carried out depending on risk factors. Many pulmonary nodules are discovered each&#xD;
           year, most of which are benign. The challenge is to distinguish malignant lesions from&#xD;
           the multitude of benign lesions. One of the most effective criteria is the doubling time&#xD;
           of the nodules which leads to multiple follow-up examinations requiring ionizing&#xD;
           radiation to assess the size and growth of the nodules. Great efforts are currently&#xD;
           being made by CT manufacturers in order to reduce the radiation with equivalent&#xD;
           diagnostic performance. Patients who were referred to our department for an unenhanced&#xD;
           low-dose chest CT (LD CT) for pulmonary nodules check-up or follow-up, and had consented&#xD;
           to participate in the study, will undergo an additional ultra-low dose acquisition&#xD;
           (ULDCT, &lt;0,25 mSv, similar to standard two-view chest X-Ray) with deep learning-based&#xD;
           reconstruction (DLIR). The main objective of this study is to evaluate the diagnostic&#xD;
           performance between ULD and LD CT protocols for the detection of pulmonary nodules. The&#xD;
           impact of dose reduction will be assessed in this context. The data from each&#xD;
           examination will be blindly interpreted from the results of the other one. No follow-up&#xD;
           will be required for the study.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Active, not recruiting</overall_status>
  <start_date type="Actual">July 22, 2020</start_date>
  <completion_date type="Anticipated">July 2023</completion_date>
  <primary_completion_date type="Actual">July 1, 2022</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <intervention_model_description>prospective transversal monocentric study</intervention_model_description>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Diagnostic accuracy</measure>
    <time_frame>Day 0</time_frame>
    <description>The study aimed to investigate the diagnostic accuracy (Sensibility and Specificity) of ultra-low dose CT using DLIR reconstruction for the detection of pulmonary nodules in comparison with the low dose CT reference protocol.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Image quality</measure>
    <time_frame>Day 0</time_frame>
    <description>The signal-to-noise ratio or SNR is calculated on areas of interest placed manually on the image (pulmonary parenchyma, axillary fat and surrounding air).&#xD;
This ratio is calculated by the average signal strength in these areas, divided by the standard deviation of the signal in outdoor areas such as the surrounding air.&#xD;
The quality of the image is estimated by a score ranging from 0 (poor quality) to 3 (excellent quality) determined subjectively by the operator.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Pulmonary nodules volume</measure>
    <time_frame>Day 0</time_frame>
    <description>Difference of pulmonary nodules volume between images acquired at low dose CT and ultra-low dose CT.</description>
  </secondary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">70</enrollment>
  <condition>Pulmonary Nodules, Multiple</condition>
  <arm_group>
    <arm_group_label>ultra-low dose CT</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination.</description>
  </arm_group>
  <intervention>
    <intervention_type>Radiation</intervention_type>
    <intervention_name>ULD CT</intervention_name>
    <description>All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination.</description>
    <arm_group_label>ultra-low dose CT</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age ≥ 18 years old,&#xD;
&#xD;
          -  Patient referred for non-enhanced chest CT for lung nodule check-up or follow-up,&#xD;
&#xD;
          -  Affiliation to a social security program,&#xD;
&#xD;
          -  Ability of the subject to understand and express opposition&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Age &lt;18 years old,&#xD;
&#xD;
          -  Person under guardianship or curatorship,&#xD;
&#xD;
          -  Pregnant woman,&#xD;
&#xD;
          -  Any contraindications to CT&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>CHU Amiens-Picardie</name>
      <address>
        <city>Amiens</city>
        <zip>80000</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>France</country>
  </location_countries>
  <verification_date>March 2023</verification_date>
  <study_first_submitted>July 15, 2020</study_first_submitted>
  <study_first_submitted_qc>July 20, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 22, 2020</study_first_posted>
  <last_update_submitted>March 21, 2023</last_update_submitted>
  <last_update_submitted_qc>March 21, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">March 22, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>lung nodules</keyword>
  <keyword>pulmonary nodules</keyword>
  <keyword>lung cancer</keyword>
  <keyword>radiation dose</keyword>
  <keyword>dose reduction</keyword>
  <keyword>low dose CT</keyword>
  <keyword>ultra-low dose CT</keyword>
  <keyword>DLIR</keyword>
  <keyword>deep learning reconstruction</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/NCT04482114
org study id: PI2020_843_0052
nct id: NCT04482114
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
evaluate the diagnostic performance of ultra-low dose CT using deep learning-based
      reconstruction in the detection of pulmonary nodules.
-  Background: Lung cancer is the leading cause of cancer deaths. Patients with pulmonary
           nodules often undergo multiple computed tomography (CT) examinations for diagnostic and
           follow-up purposes.

        -  Purpose: The main objective of this study is to evaluate the diagnostic performance of
           ultra-low dose CT using deep learning-based reconstruction in the detection of pulmonary
           nodules.

        -  Abstract: Despite recent advances, lung cancer remains the most commonly diagnosed
           cancer and the leading cause of cancer death worldwide because it is often diagnosed at
           advanced stages that are not surgically curable. Nevertheless, early detection of lung
           cancer allows surgical resection, offers curative treatment and the best chance of
           survival. There is currently no screening program in France, but individual screening
           can be carried out depending on risk factors. Many pulmonary nodules are discovered each
           year, most of which are benign. The challenge is to distinguish malignant lesions from
           the multitude of benign lesions. One of the most effective criteria is the doubling time
           of the nodules which leads to multiple follow-up examinations requiring ionizing
           radiation to assess the size and growth of the nodules. Great efforts are currently
           being made by CT manufacturers in order to reduce the radiation with equivalent
           diagnostic performance. Patients who were referred to our department for an unenhanced
           low-dose chest CT (LD CT) for pulmonary nodules check-up or follow-up, and had consented
           to participate in the study, will undergo an additional ultra-low dose acquisition
           (ULDCT, <0,25 mSv, similar to standard two-view chest X-Ray) with deep learning-based
           reconstruction (DLIR). The main objective of this study is to evaluate the diagnostic
           performance between ULD and LD CT protocols for the detection of pulmonary nodules. The
           impact of dose reduction will be assessed in this context. The data from each
           examination will be blindly interpreted from the results of the other one. No follow-up
           will be required for the study.
allocation: N/A
intervention model: Single Group Assignment
intervention model description: prospective transversal monocentric study
primary purpose: Diagnostic
masking: None (Open Label)
measure: Diagnostic accuracy
time frame: Day 0
description: The study aimed to investigate the diagnostic accuracy (Sensibility and Specificity) of ultra-low dose CT using DLIR reconstruction for the detection of pulmonary nodules in comparison with the low dose CT reference protocol.
measure: Image quality
time frame: Day 0
description: The signal-to-noise ratio or SNR is calculated on areas of interest placed manually on the image (pulmonary parenchyma, axillary fat and surrounding air).
This ratio is calculated by the average signal strength in these areas, divided by the standard deviation of the signal in outdoor areas such as the surrounding air.
The quality of the image is estimated by a score ranging from 0 (poor quality) to 3 (excellent quality) determined subjectively by the operator.
measure: Pulmonary nodules volume
time frame: Day 0
description: Difference of pulmonary nodules volume between images acquired at low dose CT and ultra-low dose CT.
arm group label: ultra-low dose CT
arm group type: Other
description: All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination.
intervention type: Radiation
intervention name: ULD CT
description: All the examinations are part of the routine care. Addition of the ULD CT protocol does not require injection of contrast agent and does not extend the duration of the examination.
arm group label: ultra-low dose CT
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
facility: 
country: France
responsible party type: Sponsor
mesh term: Multiple Pulmonary Nodules