Improving Detection of Early Lung Cancer in a Diverse Population (IDEAL) Study

The goal of this trial is to (a) identify people at high risk of lung cancer who would benefit from LDCT screening but are currently ineligible based on current lung cancer screening criteria (b) provide the framework to manage patients with Incidental Pulmonary Nodules (IPNs) with appropriate follow-up based on accurate interpretation of the Chest CT scan that is already available and (c) develop a simple, point-of-care, minimally invasive test, focusing on the breath and circulating blood proteins, to detect lung cancer, and develop a method to differentiate between cancerous and non-cancerous nodules using a single. Participants will be asked to answer a questionnaire regarding their age, race/ethnicity, smoking history, and residential history if they have ever been told they have chronic obstructive pulmonary disease (COPD), high blood pressure, education level, medications and height and weight. Participants will then be asked to give a breath sample via the breath collection device. All this information will be collected before the breath collection. After that, participants will give 1-2 tablespoons of blood. CT scans with IPN(s) will be reviewed and run through a computer detection software to identify nodules, followed up as per current clinical guidelines.

Lung cancer continues to be the leading cause of cancer death in Canada and worldwide. Low-dose computed tomography (LDCT) screening has been shown to be a cost-effective measure to save lives in people who have smoked heavily based on several large randomized control trials. The evidence led to the implementation of LDCT lung cancer screening in Canada for people who have smoked heavily in the past or are still smoking. However, over 50% of patients who develop lung cancer today would not meet current screening eligibility criteria. A strategy to identify individuals who are not currently eligible for screening but are at high risk for lung cancer is urgently needed. Meanwhile, recent studies have shown that patients with incidental pulmonary nodules (IPNs), often do not meet LDCT screening criteria. These patients despite having a higher risk of lung cancer, the majority are never followed up in the clinical setting. This represents an important opportunity to address the emerging challenge for lung cancer early detection while addressing the missing link between IPNs and lung cancer. 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.