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/NCT05968898</url>
    </required_header>
    <id_info>
      <org_study_id>10523</org_study_id>
      <nct_id>NCT05968898</nct_id>
    </id_info>
    <brief_title>Radiomics Tool for Pulmonary Nodule Risk Stratification</brief_title>
    <official_title>Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Abramson Cancer Center at Penn Medicine</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Abramson Cancer Center at Penn Medicine</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>Yes</is_fda_regulated_device>
      <is_us_export>No</is_us_export>
    </oversight_info>
    <brief_summary>
      <textblock>
        This is a pragmatic clinical trial that will study the effect of a radiomics-based&#xD;
        computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs)&#xD;
        compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN&#xD;
        clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy&#xD;
        risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care +&#xD;
        use of a radiomics-based CAD tool.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Accurate malignancy risk stratification of pulmonary nodules (PNs) is critical to ensuring&#xD;
        that cancer is diagnosed in a timely manner and patients do not undergo unnecessary&#xD;
        diagnostic procedures. Preliminary data suggests that a radiomics-based lung cancer&#xD;
        prediction (LCP) computer-aided diagnosis (CAD) tool is effective in risk stratifying PNs and&#xD;
        may improve clinicians' PN management decisions. This is a pragmatic clinical trial&#xD;
        evaluating the effect of this CAD tool on clinicians' management of PNs compared to usual&#xD;
        care. Individuals eligible for this study will include adults aged 35-89 years who are&#xD;
        scheduled to be evaluated at a Penn Medicine PN clinic for a newly discovered PN 8-30mm in&#xD;
        maximal diameter on CT imaging. Exclusion criteria include lack of CT imaging data at the&#xD;
        time of index clinic visit, thoracic lymphadenopathy by CT size criteria, presence of&#xD;
        pulmonary masses (&gt;3cm in maximal diameter), PNs with popcorn calcification (consistent with&#xD;
        benign etiology), subsolid PNs, and a known history of active cancer. Enrolled participants&#xD;
        will undergo 1:1 stratified randomization to one of two groups, defined by the PN malignancy&#xD;
        risk stratification strategy used by evaluating clinicians: 1) usual care (clinician&#xD;
        assessment) or 2) clinician assessment + CAD-based risk stratification using the LCP-CAD&#xD;
        tool. The control arm will be usual care, defined as routine clinician assessment of PN&#xD;
        malignancy risk. In the experimental arm, at the start of the clinic visit clinicians will be&#xD;
        provided a report with the CAD tool estimate of malignancy risk for the PN being evaluated&#xD;
        along with information about the CAD tool.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">January 1, 2024</start_date>
    <completion_date type="Anticipated">December 31, 2027</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2026</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <primary_purpose>Diagnostic</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Appropriate management of pulmonary nodule</measure>
      <time_frame>12 months</time_frame>
      <description>The composite proportion of benign pulmonary nodules managed with imaging surveillance and malignant pulmonary nodules managed with biopsy or empiric treatment. Final pulmonary nodule diagnosis will be categorized as malignant or benign. If the pathology report does not offer a specific pathologic diagnosis or in inconclusive (i.e., the biopsy was non-diagnostic), we will defined pulmonary nodule resolution, shrinkage, or diameter stability at 12 months as a benign diagnosis.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Timeliness of care</measure>
      <time_frame>12 months</time_frame>
      <description>For patients with malignant pulmonary nodules, defined as the number of days between the index clinic visit and diagnosis of malignancy and receipt of treatment for malignancy (i.e., surgical resection, radiation therapy).</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Adverse events</measure>
      <time_frame>12 months</time_frame>
      <description>For patients undergoing biopsy, defined as procedural complications related to pulmonary nodule biopsy.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Diagnostic yield</measure>
      <time_frame>12 months</time_frame>
      <description>Using information found in pathology reports, defined as the proportion of biopsies with a definitive histopathologic diagnosis, for each type of diagnostic biopsy procedure.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Healthcare costs</measure>
      <time_frame>12 months</time_frame>
      <description>The costs of all imaging studies and diagnostic testing associated with the pulmonary nodule diagnostic process, based on Medicare allowed amounts (amount paid by Medicare and the amount paid by the beneficiary and/or third parties).</description>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">300</enrollment>
    <condition>Lung Cancer</condition>
    <condition>Pulmonary Nodule, Solitary</condition>
    <arm_group>
      <arm_group_label>Usual care (clinician assessment)</arm_group_label>
      <arm_group_type>No Intervention</arm_group_type>
      <description>In the usual care arm, clinicians will evaluate individuals with indeterminate pulmonary nodules as part of routine clinical care. No specific guidance regarding pulmonary nodule risk stratification will provided to evaluating clinicians.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Clinician assessment + CAD-based risk stratification</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>In the experimental arm, evaluating clinicians will receive an electronic health record-based alert with the report from an artificial intelligence radiomics-based computer-aided diagnosis tool for risk stratification of pulmonary nodules.</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Optellum Virtual Nodule Clinic</intervention_name>
      <description>The Optellum Virtual Nodule Clinic is an FDA-approved (Class II) device for risk stratification of pulmonary nodules. It uses a convolutional neural network to evaluate CT imaging data to provide an estimate of malignancy risk for indeterminate pulmonary nodules.</description>
      <arm_group_label>Clinician assessment + CAD-based risk stratification</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Male or female, aged 35-89 years&#xD;
  &#xD;
            2. Scheduled to be evaluated at a pulmonary nodule clinic&#xD;
  &#xD;
            3. Newly discovered solid PN 8-30mm in maximal diameter on CT imaging&#xD;
  &#xD;
            4. Chest CT imaging compatible with Optellum Virtual Nodule Clinic software and available&#xD;
               on or before the date of index clinic visit&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Chest CT imaging with mediastinal or hilar lymphadenopathy by CT size criteria (&gt;10mm&#xD;
               in maximal short-axis diameter on axial CT images)&#xD;
  &#xD;
            2. PNs with popcorn calcification (consistent with benign etiology)&#xD;
  &#xD;
            3. Subsolid PNs (may be associated with lower risk of clinically significant malignancy)&#xD;
  &#xD;
            4. Known history of active cancer&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>35 Years</minimum_age>
      <maximum_age>89 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Roger Y. Kim, MD, MSCE</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Pennsylvania</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Roger Y. Kim, MD, MSCE</last_name>
      <phone>215-662-3677</phone>
      <email>roger.kim@pennmedicine.upenn.edu</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Anil Vachani, MD, MSCE</last_name>
      <phone>215-573-7931</phone>
      <email>avachani@pennmedicine.upenn.edu</email>
    </overall_contact_backup>
    <reference>
      <citation>Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022 Sep;304(3):683-691. doi: 10.1148/radiol.212182. Epub 2022 May 24.</citation>
      <PMID>35608444</PMID>
    </reference>
    <reference>
      <citation>Kim RY, Oke JL, Dotson TL, Bellinger CR, Vachani A. Effect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules. Respirology. 2023 Jun;28(6):582-584. doi: 10.1111/resp.14502. Epub 2023 Apr 5. No abstract available.</citation>
      <PMID>37017091</PMID>
    </reference>
    <reference>
      <citation>Massion PP, Antic S, Ather S, Arteta C, Brabec J, Chen H, Declerck J, Dufek D, Hickes W, Kadir T, Kunst J, Landman BA, Munden RF, Novotny P, Peschl H, Pickup LC, Santos C, Smith GT, Talwar A, Gleeson F. Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. Am J Respir Crit Care Med. 2020 Jul 15;202(2):241-249. doi: 10.1164/rccm.201903-0505OC.</citation>
      <PMID>32326730</PMID>
    </reference>
    <reference>
      <citation>Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.</citation>
      <PMID>32139611</PMID>
    </reference>
    <reference>
      <citation>Paez R, Kammer MN, Balar A, Lakhani DA, Knight M, Rowe D, Xiao D, Heideman BE, Antic SL, Chen H, Chen SC, Peikert T, Sandler KL, Landman BA, Deppen SA, Grogan EL, Maldonado F. Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep. 2023 Apr 15;13(1):6157. doi: 10.1038/s41598-023-33098-y.</citation>
      <PMID>37061539</PMID>
    </reference>
    <reference>
      <citation>Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest. 2023 May 25:S0012-3692(23)00785-7. doi: 10.1016/j.chest.2023.05.025. Online ahead of print.</citation>
      <PMID>37244587</PMID>
    </reference>
    <verification_date>July 2023</verification_date>
    <study_first_submitted>July 21, 2023</study_first_submitted>
    <study_first_submitted_qc>July 28, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 1, 2023</study_first_posted>
    <last_update_submitted>July 28, 2023</last_update_submitted>
    <last_update_submitted_qc>July 28, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 1, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Abramson Cancer Center at Penn Medicine</investigator_affiliation>
      <investigator_full_name>Roger Y. Kim</investigator_full_name>
      <investigator_title>Instructor of Medicine</investigator_title>
    </responsible_party>
    <keyword>clinical effectiveness</keyword>
    <keyword>clinical utility</keyword>
    <keyword>artificial intelligence</keyword>
    <keyword>medical decision-making</keyword>
    <keyword>risk stratification</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Multiple Pulmonary Nodules</mesh_term>
      <mesh_term>Solitary Pulmonary Nodule</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/NCT05968898
  org study id: 10523
  nct id: NCT05968898
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: Yes
  is us export: No
  This is a pragmatic clinical trial that will study the effect of a radiomics-based
        computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs)
        compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN
        clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy
        risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care +
        use of a radiomics-based CAD tool.
  Accurate malignancy risk stratification of pulmonary nodules (PNs) is critical to ensuring
        that cancer is diagnosed in a timely manner and patients do not undergo unnecessary
        diagnostic procedures. Preliminary data suggests that a radiomics-based lung cancer
        prediction (LCP) computer-aided diagnosis (CAD) tool is effective in risk stratifying PNs and
        may improve clinicians' PN management decisions. This is a pragmatic clinical trial
        evaluating the effect of this CAD tool on clinicians' management of PNs compared to usual
        care. Individuals eligible for this study will include adults aged 35-89 years who are
        scheduled to be evaluated at a Penn Medicine PN clinic for a newly discovered PN 8-30mm in
        maximal diameter on CT imaging. Exclusion criteria include lack of CT imaging data at the
        time of index clinic visit, thoracic lymphadenopathy by CT size criteria, presence of
        pulmonary masses (>3cm in maximal diameter), PNs with popcorn calcification (consistent with
        benign etiology), subsolid PNs, and a known history of active cancer. Enrolled participants
        will undergo 1:1 stratified randomization to one of two groups, defined by the PN malignancy
        risk stratification strategy used by evaluating clinicians: 1) usual care (clinician
        assessment) or 2) clinician assessment + CAD-based risk stratification using the LCP-CAD
        tool. The control arm will be usual care, defined as routine clinician assessment of PN
        malignancy risk. In the experimental arm, at the start of the clinic visit clinicians will be
        provided a report with the CAD tool estimate of malignancy risk for the PN being evaluated
        along with information about the CAD tool.
  allocation: Randomized
  intervention model: Parallel Assignment
  primary purpose: Diagnostic
  masking: None (Open Label)
  measure: Appropriate management of pulmonary nodule
  time frame: 12 months
  description: The composite proportion of benign pulmonary nodules managed with imaging surveillance and malignant pulmonary nodules managed with biopsy or empiric treatment. Final pulmonary nodule diagnosis will be categorized as malignant or benign. If the pathology report does not offer a specific pathologic diagnosis or in inconclusive (i.e., the biopsy was non-diagnostic), we will defined pulmonary nodule resolution, shrinkage, or diameter stability at 12 months as a benign diagnosis.
  measure: Timeliness of care
  time frame: 12 months
  description: For patients with malignant pulmonary nodules, defined as the number of days between the index clinic visit and diagnosis of malignancy and receipt of treatment for malignancy (i.e., surgical resection, radiation therapy).
  measure: Adverse events
  time frame: 12 months
  description: For patients undergoing biopsy, defined as procedural complications related to pulmonary nodule biopsy.
  measure: Diagnostic yield
  time frame: 12 months
  description: Using information found in pathology reports, defined as the proportion of biopsies with a definitive histopathologic diagnosis, for each type of diagnostic biopsy procedure.
  measure: Healthcare costs
  time frame: 12 months
  description: The costs of all imaging studies and diagnostic testing associated with the pulmonary nodule diagnostic process, based on Medicare allowed amounts (amount paid by Medicare and the amount paid by the beneficiary and/or third parties).
  arm group label: Usual care (clinician assessment)
  arm group type: No Intervention
  description: In the usual care arm, clinicians will evaluate individuals with indeterminate pulmonary nodules as part of routine clinical care. No specific guidance regarding pulmonary nodule risk stratification will provided to evaluating clinicians.
  arm group label: Clinician assessment + CAD-based risk stratification
  arm group type: Experimental
  description: In the experimental arm, evaluating clinicians will receive an electronic health record-based alert with the report from an artificial intelligence radiomics-based computer-aided diagnosis tool for risk stratification of pulmonary nodules.
  intervention type: Device
  intervention name: Optellum Virtual Nodule Clinic
  description: The Optellum Virtual Nodule Clinic is an FDA-approved (Class II) device for risk stratification of pulmonary nodules. It uses a convolutional neural network to evaluate CT imaging data to provide an estimate of malignancy risk for indeterminate pulmonary nodules.
  arm group label: Clinician assessment + CAD-based risk stratification
  criteria: 
  gender: All
  minimum age: 35 Years
  maximum age: 89 Years
  healthy volunteers: No
  last name: Roger Y. Kim, MD, MSCE
  role: Principal Investigator
  affiliation: University of Pennsylvania
  last name: Roger Y. Kim, MD, MSCE
  phone: 215-662-3677
  email: roger.kim@pennmedicine.upenn.edu
  last name: Anil Vachani, MD, MSCE
  phone: 215-573-7931
  email: avachani@pennmedicine.upenn.edu
  citation: Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022 Sep;304(3):683-691. doi: 10.1148/radiol.212182. Epub 2022 May 24.
  PMID: 35608444
  citation: Kim RY, Oke JL, Dotson TL, Bellinger CR, Vachani A. Effect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules. Respirology. 2023 Jun;28(6):582-584. doi: 10.1111/resp.14502. Epub 2023 Apr 5. No abstract available.
  PMID: 37017091
  citation: Massion PP, Antic S, Ather S, Arteta C, Brabec J, Chen H, Declerck J, Dufek D, Hickes W, Kadir T, Kunst J, Landman BA, Munden RF, Novotny P, Peschl H, Pickup LC, Santos C, Smith GT, Talwar A, Gleeson F. Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. Am J Respir Crit Care Med. 2020 Jul 15;202(2):241-249. doi: 10.1164/rccm.201903-0505OC.
  PMID: 32326730
  citation: Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.
  PMID: 32139611
  citation: Paez R, Kammer MN, Balar A, Lakhani DA, Knight M, Rowe D, Xiao D, Heideman BE, Antic SL, Chen H, Chen SC, Peikert T, Sandler KL, Landman BA, Deppen SA, Grogan EL, Maldonado F. Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep. 2023 Apr 15;13(1):6157. doi: 10.1038/s41598-023-33098-y.
  PMID: 37061539
  citation: Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest. 2023 May 25:S0012-3692(23)00785-7. doi: 10.1016/j.chest.2023.05.025. Online ahead of print.
  PMID: 37244587
  responsible party type: Principal Investigator
  investigator affiliation: Abramson Cancer Center at Penn Medicine
  investigator full name: Roger Y. Kim
  investigator title: Instructor of Medicine
  mesh term: Multiple Pulmonary Nodules
  mesh term: Solitary Pulmonary Nodule
  sharing ipd: No