Improving Detection of Early Lung Cancer in a Diverse Population (IDEAL) Study
NCT ID: NCT06628102
Last Updated: 2024-10-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
3600 participants
INTERVENTIONAL
2023-08-04
2028-12-31
Brief Summary
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Detailed Description
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Objective.
Our proposed study will have two important goals: (a) identify people at high risk of lung cancer who would benefit from LDCT screening but are currently ineligible; (b) provide the framework to manage patients with Incidental Pulmonary Nodules with appropriate follow-up based on accurate interpretation of the chest CT scan that is already available.
Specific Aims
(i) Develop the framework to assess the lung cancer risk of IPN patients; (ii) Develop and validate an exhaled breath test to identify high-risk individuals who do not meet current eligibility criteria for LDCT screening; (iii) Prospectively validate a panel of circulating blood proteins to personalize the management of incidental pulmonary nodules; (iv) Improve the accuracy and consistency of reading chest CT scans with incidental pulmonary nodules to improve lung cancer early detection; (v) Evaluate the health-economic benefits of a multi-modal approach to the management of incidental pulmonary nodules
Methods. In total, there will be 3600 participants recruited between both arms of the study.
As IPN patients represent a high-risk, screening ineligible and usually under-served population, we will establish a multi-provincial IPN cohort based on 3600 patients with IPNs from 3 provinces: British Columbia, Ontario and Quebec. For the Breathomics, we will conduct a comprehensive investigation of volatile organic compounds using state-of-the-art technology, and high-resolution accurate mass gas chromatography spectrometry. The breath signatures will be validated using pre-diagnostic breath samples in the IPN cohort and test the clinical utility with a point-of-care mobile device. We will validate the circulating protein panel prospectively and assess the absolute risk of lung cancer in the IPN cohort to establish the risk thresholds for clinical application. In parallel, we will develop a deep learning algorithm based on LDCT images to improve the classification of IPN. A health economic analysis will be performed on the clinical utility of these tools.
Significance:
The IPN population provides an untapped learning opportunity for us to improve lung cancer early detection. Identification of individuals who are not currently eligible for screening but are at high risk of lung cancer allows us to utilize artificial intelligence and other biomarkers to predict lung cancer from an already available single chest CT that is already available.
Conditions
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Study Design
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NA
SINGLE_GROUP
SCREENING
NONE
Study Groups
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Incidental Pulmonary Nodule Arm
Nodule Follow up as per Fleischner Guidelines
Chest CT Scan
Chest CT Scan
Interventions
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Chest CT Scan
Chest CT Scan
Eligibility Criteria
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Inclusion Criteria
* Able to consent
* Chest CT positive for nodule equal to or greater than 6mm
* No additional other cancer- (outside of lung cancer for group 1)
* Must be able to abstain from smoking tobacco for 24 hours prior to the breath test.
Exclusion Criteria
* you have smoked in the last 24 hours
* You are pregnant
* You have been diagnosed with a respiratory infection in the last 3 months
* Unwilling to consent to the study
50 Years
80 Years
ALL
Yes
Sponsors
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Laval University
OTHER
Lunenfeld Tanenbaum Research Institute
OTHER
Canadian Cancer Society (CCS)
OTHER
University Health Network, Toronto
OTHER
British Columbia Cancer Agency
OTHER
Responsible Party
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Renelle Myers
Principal Investigator
Principal Investigators
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Renelle L Myers, MD
Role: PRINCIPAL_INVESTIGATOR
VCH, UBC
Rayjean Hung, PhD
Role: PRINCIPAL_INVESTIGATOR
Sinai Health
Locations
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BC Cancer Research, part of the Provincial Health Authority
Vancouver, British Columbia, Canada
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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707695
Identifier Type: -
Identifier Source: org_study_id
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