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Radiomics Tool for Pulmonary Nodule Risk Stratification

Primary Purpose

Lung Cancer, Pulmonary Nodule, Solitary

Status
Not yet recruiting
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
Optellum Virtual Nodule Clinic
Sponsored by
Abramson Cancer Center at Penn Medicine
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Lung Cancer focused on measuring clinical effectiveness, clinical utility, artificial intelligence, medical decision-making, risk stratification

Eligibility Criteria

35 Years - 89 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: Male or female, aged 35-89 years Scheduled to be evaluated at a pulmonary nodule clinic Newly discovered solid PN 8-30mm in maximal diameter on CT imaging Chest CT imaging compatible with Optellum Virtual Nodule Clinic software and available on or before the date of index clinic visit Exclusion Criteria: Chest CT imaging with mediastinal or hilar lymphadenopathy by CT size criteria (>10mm in maximal short-axis diameter on axial CT images) PNs with popcorn calcification (consistent with benign etiology) Subsolid PNs (may be associated with lower risk of clinically significant malignancy) Known history of active cancer

Sites / Locations

    Arms of the Study

    Arm 1

    Arm 2

    Arm Type

    No Intervention

    Experimental

    Arm Label

    Usual care (clinician assessment)

    Clinician assessment + CAD-based risk stratification

    Arm 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.

    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.

    Outcomes

    Primary Outcome Measures

    Appropriate management of pulmonary nodule
    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.

    Secondary Outcome Measures

    Timeliness of care
    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).
    Adverse events
    For patients undergoing biopsy, defined as procedural complications related to pulmonary nodule biopsy.
    Diagnostic yield
    Using information found in pathology reports, defined as the proportion of biopsies with a definitive histopathologic diagnosis, for each type of diagnostic biopsy procedure.
    Healthcare costs
    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).

    Full Information

    First Posted
    July 21, 2023
    Last Updated
    July 28, 2023
    Sponsor
    Abramson Cancer Center at Penn Medicine
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    1. Study Identification

    Unique Protocol Identification Number
    NCT05968898
    Brief Title
    Radiomics Tool for Pulmonary Nodule Risk Stratification
    Official Title
    Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
    Study Type
    Interventional

    2. Study Status

    Record Verification Date
    July 2023
    Overall Recruitment Status
    Not yet recruiting
    Study Start Date
    January 1, 2024 (Anticipated)
    Primary Completion Date
    December 31, 2026 (Anticipated)
    Study Completion Date
    December 31, 2027 (Anticipated)

    3. Sponsor/Collaborators

    Responsible Party, by Official Title
    Principal Investigator
    Name of the Sponsor
    Abramson Cancer Center at Penn Medicine

    4. Oversight

    Studies a U.S. FDA-regulated Drug Product
    No
    Studies a U.S. FDA-regulated Device Product
    Yes
    Product Manufactured in and Exported from the U.S.
    No
    Data Monitoring Committee
    No

    5. Study Description

    Brief Summary
    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.
    Detailed Description
    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.

    6. Conditions and Keywords

    Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
    Lung Cancer, Pulmonary Nodule, Solitary
    Keywords
    clinical effectiveness, clinical utility, artificial intelligence, medical decision-making, risk stratification

    7. Study Design

    Primary Purpose
    Diagnostic
    Study Phase
    Not Applicable
    Interventional Study Model
    Parallel Assignment
    Masking
    None (Open Label)
    Allocation
    Randomized
    Enrollment
    300 (Anticipated)

    8. Arms, Groups, and Interventions

    Arm Title
    Usual care (clinician assessment)
    Arm Type
    No Intervention
    Arm 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 Title
    Clinician assessment + CAD-based risk stratification
    Arm Type
    Experimental
    Arm 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(s)
    Optellum Virtual Nodule Clinic
    Intervention 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.
    Primary Outcome Measure Information:
    Title
    Appropriate management of pulmonary nodule
    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.
    Time Frame
    12 months
    Secondary Outcome Measure Information:
    Title
    Timeliness of care
    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).
    Time Frame
    12 months
    Title
    Adverse events
    Description
    For patients undergoing biopsy, defined as procedural complications related to pulmonary nodule biopsy.
    Time Frame
    12 months
    Title
    Diagnostic yield
    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.
    Time Frame
    12 months
    Title
    Healthcare costs
    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).
    Time Frame
    12 months

    10. Eligibility

    Sex
    All
    Minimum Age & Unit of Time
    35 Years
    Maximum Age & Unit of Time
    89 Years
    Accepts Healthy Volunteers
    No
    Eligibility Criteria
    Inclusion Criteria: Male or female, aged 35-89 years Scheduled to be evaluated at a pulmonary nodule clinic Newly discovered solid PN 8-30mm in maximal diameter on CT imaging Chest CT imaging compatible with Optellum Virtual Nodule Clinic software and available on or before the date of index clinic visit Exclusion Criteria: Chest CT imaging with mediastinal or hilar lymphadenopathy by CT size criteria (>10mm in maximal short-axis diameter on axial CT images) PNs with popcorn calcification (consistent with benign etiology) Subsolid PNs (may be associated with lower risk of clinically significant malignancy) Known history of active cancer
    Central Contact Person:
    First Name & Middle Initial & Last Name or Official Title & Degree
    Roger Y. Kim, MD, MSCE
    Phone
    215-662-3677
    Email
    roger.kim@pennmedicine.upenn.edu
    First Name & Middle Initial & Last Name or Official Title & Degree
    Anil Vachani, MD, MSCE
    Phone
    215-573-7931
    Email
    avachani@pennmedicine.upenn.edu
    Overall Study Officials:
    First Name & Middle Initial & Last Name & Degree
    Roger Y. Kim, MD, MSCE
    Organizational Affiliation
    University of Pennsylvania
    Official's Role
    Principal Investigator

    12. IPD Sharing Statement

    Plan to Share IPD
    No
    Citations:
    PubMed Identifier
    35608444
    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.
    Results Reference
    background
    PubMed Identifier
    37017091
    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.
    Results Reference
    background
    PubMed Identifier
    32326730
    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.
    Results Reference
    background
    PubMed Identifier
    32139611
    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.
    Results Reference
    background
    PubMed Identifier
    37061539
    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.
    Results Reference
    background
    PubMed Identifier
    37244587
    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 Oct;164(4):1028-1041. doi: 10.1016/j.chest.2023.05.025. Epub 2023 May 25.
    Results Reference
    background

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