[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100659916":3},{"organization":4,"outcomesModule":7,"designInfo":22,"detailedDescription":25,"studyPopulation":26,"armGroups":27,"interventions":37,"overallOfficials":45,"centralContacts":50,"locations":56,"responsibleParty":72,"collaborators":74,"id":78,"slug":79,"hasResults":80,"nctId":81,"briefTitle":82,"officialTitle":83,"acronym":21,"eligibilityCriteria":84,"healthyVolunteers":80,"sex":85,"minAge":86,"maxAge":21,"enrollmentInfo":87,"targetDuration":21,"studyType":90,"phases":21,"briefSummary":91,"conditions":92,"keywords":94,"overallStatus":58,"whyStopped":21,"lastUpdateSubmitDate":99,"lastUpdatePostDateStruct":100,"startDateStruct":103,"completionDateStruct":105,"leadSponsor":107,"locationsCount":108},{"fullName":5,"class":6},"Thomas Jefferson University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":16,"otherOutcomes":21},[9,13],{"measure":10,"description":11,"timeFrame":12},"Sensitivity of the Biosensor Platform (BSP) for Detection of Lung Cancer in the Jefferson Health Cohort","Percentage of participants with lung cancer who have positive Biosensor Platform (BSP) result. Sensitivity will be estimated using exact logistic regression and reported with 95% confidence interval.","Up to 24 months",{"measure":14,"description":15,"timeFrame":12},"Specificity of the Biosensor Platform (BSP) for Detection of Lung Cancer in the Jefferson Health Cohort","Percentage of participants without lung cancer who have a negative Biosensor Platform (BSP) result. Specificity will be estimated using exact logistic regression and reported with 95% confidence interval.",[17,19],{"measure":18,"description":11,"timeFrame":12},"Sensitivity of the Biosensor Platform (BSP) for Detection of Lung Cancer in the Combined Jefferson Health Cohort and EARLY Labs Cohort",{"measure":20,"description":15,"timeFrame":12},"Specificity of the Biosensor Platform (BSP) for Detection of Lung Cancer in the Combined Jefferson Health Cohort and EARLY Labs Cohort",null,{"allocation":21,"interventionModel":21,"interventionModelDescription":21,"primaryPurpose":21,"observationalModel":23,"timePerspective":24,"maskingInfo":21},"CASE_CONTROL","PROSPECTIVE","Lung cancer is the leading cause of cancer deaths worldwide, and most lung cancers continue to be diagnosed at advanced stages of disease. Current methods for screening high-risk individuals with annual LDCT are insufficient, as evidenced by low rates of both screening eligibility and uptake. A non-invasive method for early detection is needed for populations currently excluded from screening. Although several blood-based biomarkers for lung cancer screening (LCS) are being tested in clinical trials enrolling LCS-eligible cohorts, there are no validated biomarkers for individuals who have never smoked. The investigators propose to test a VOC signature using a novel urine biosensor platform in a cohort of prospectively recruited patients who have never smoked, with and without lung cancer. Successful completion of the project would provide proof-of-concept in an independent cohort and propel this technology into future multi-center validation studies for lung cancer detection. The investigator aim to apply this biomarker to ultimately advance screening opportunities for LCS-ineligible individuals, an understudied population with an urgent need for earlier diagnosis.\n\nEighty-seven patients at least 18 years of age without a smoking history (defined as fewer than 100 lifetime cigarettes), undergoing evaluation for an indeterminate pulmonary nodule ≥6mm on CT chest and planned for a lung biopsy or surgical resection at TJU CC and MHD, without previous diagnosis of or treatment for any type of cancer.\n\nThe overall objective of the proposed project is to test a VOC signature using a urine biosensor platform in a prospective case-control study of patients who have never smoked and with moderate-risk lung nodules.\n\nPrimary: Determine test performance (sensitivity and specificity) in a case-control cohort.\n\nSecondary: Determine test performance (sensitivity and specificity) in a combined cohort of Jefferson Health patients (Objective 1) and EARLY Labs Biobank participants.\n\nExploratory Objectives: 1) Identify test performance among high-risk subgroups based on clinical factors including age, gender, family history of lung cancer, adenocarcinoma histology. 2) Determine reclassification rate of moderate-risk lung nodules into high- vs. low-risk groups. 3) Assess whether the combination of BSP test result with clinical lung nodule risk prediction score improves predictive accuracy.","Eighty-seven patients at least 18 years of age without a smoking history (defined as fewer than 100 lifetime cigarettes), undergoing evaluation for an indeterminate pulmonary nodule ≥6mm on CT chest and planned for a lung biopsy or surgical resection at TJU CC and MHD, without previous diagnosis of or treatment for any type of cancer.",[28,33],{"label":29,"type":21,"description":30,"interventionNames":31},"Cohort 1: Lung Cancer Cases","Lung cancer cases will include patients with a specific malignant histopathology of primary lung cancer.",[32],"Diagnostic Test: Biosensor Platform",{"label":34,"type":21,"description":35,"interventionNames":36},"Cohort 2: Non-Cancer Controls","Non-cancer controls will include patients with a specific benign diagnosis (i.e. hamartoma, organizing pneumonia, granulomatous inflammation, infectious etiology with a specific organism on microbiologic studies) as well as nonspecific benign results (acute or chronic nonspecific inflammation or fibrosis).",[32],[38],{"type":39,"name":40,"description":41,"armGroupLabels":42,"otherNames":43},"DIAGNOSTIC_TEST","Biosensor Platform","The company developed a new system known as the \"Biosensor Platform\" (BSP). This platform integrates hardware, software, and database infrastructure designed by Early Labs. The outcomes are generated by a machine-learning algorithm powered by AI, which considers both the current and historical performance data of the biological sensors. Various controls are implemented to ensure the performance of the sensors is evaluated 100% of the time.",[29,34],[44],"BSP",[46],{"name":47,"affiliation":48,"role":49},"Julia A Barta, MD, ATSF","Sidney Kimmel Comprehensive Cancer Center at Thomas Jefferson University","PRINCIPAL_INVESTIGATOR",[51],{"name":52,"role":53,"phone":54,"phoneExt":21,"email":55},"Site Public Contact","CONTACT","215-600-9151","ONCTrialNow@jefferson.edu",[57],{"facility":48,"status":58,"city":59,"state":60,"zip":61,"country":62,"countryCode":63,"cosmosGeoPoint":64,"geoPoint":69,"contacts":70},"RECRUITING","Philadelphia","Pennsylvania","19107","United States","US",{"type":65,"coordinates":66},"Point",[67,68],-75.16362,39.95238,{"lat":68,"lon":67},[71],{"name":47,"role":53,"phone":54,"phoneExt":21,"email":55},{"type":73,"investigatorFullName":21,"investigatorTitle":21,"investigatorAffiliation":21,"oldNameTitle":21,"oldOrganization":21},"SPONSOR",[75],{"name":76,"class":77},"EARLY Labs","UNKNOWN","100659916","lung-cancer-detection-in-never-smokers-using-urine-volatile-organic-compound-vocs-100659916",false,"NCT07871656","Lung Cancer Detection in Never Smokers Using Urine Volatile Organic Compound (VOCs)","Identification Of Lung Cancer In Never-Smoking Individuals Using A Non-Invasive Volatile Organic Compound (VOCs) Biological Platform With Urine Specimens","Inclusion Criteria:\n\n* Individuals must meet all of the following inclusion criteria in order to be eligible to participate in the study:\n\n  1. Adults over 18 years of age. Participants must be legally competent to provide informed consent.\n  2. Presence of a new, solid lung nodule identified on diagnostic CT chest scan ≥6mm in maximum diameter (representing indeterminate or suspicious nodules that may be due to a benign lung etiology or suspicious for primary lung cancer). If multiple lung nodules are present, the dominant lung nodule ≥6mm will be considered the index nodule.\n  3. Neversmoking status, defined as \\\u003C100 lifetime cigarettes.\n  4. Confirmed or pending clinical diagnosis of either benign or malignant lung lesions, based on radiological and\u002For histopathological evaluation performed as part of routine care.\n  5. Able and willing to provide a urine sample of between 20 to 50 ml, collected in a sterile container under study supervision.\n  6. Able to understand the study information and provide written informed consent after adequate explanation of the study purpose, procedures, data handling, and absence of direct clinical benefit\n\nExclusion Criteria:\n\n* An individual who meets any of the following criteria will be excluded from participation in this study:\n\n  1. Pregnant or breastfeeding women. Pregnancy may alter urinary metabolite composition, potentially affecting analytical accuracy.\n  2. Patients with end-stage renal disease, defined as estimated glomerular filtration rate (eGFR) \\\u003C15mL\u002Fmin\u002F1.73m2 or the initiation of long-term dialysis or transplantation.\n  3. Previous diagnosis or treatment for any type of cancer. Prior malignancy or cancer therapy (surgery, chemotherapy, radiotherapy, biologic or immunotherapy) could influence urinary volatile compound profiles and confound results.\n  4. Active severe illness or other conditions that, in the opinion of the investigator, would interfere with participation or pose unnecessary risk.\n  5. Sample quality or collection constraints. Participants who cannot provide a urine sample within the required time window or under appropriate conditions for freezing and shipment will be excluded.","ALL","18 Years",{"count":88,"type":89},87,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to learn whether a urine-based test that measures volatile organic compounds (VOCs) can help detect lung cancer in people who have never smoked. The study will include adults who have never smoked, both with and without lung cancer.\n\nThe main questions it aims to answer are:\n\nCan a urine VOC biosensor distinguish between people who have lung cancer and those who do not? Could this urine-based biomarker serve as a non-invasive approach for the early detection of lung cancer in people who are not currently eligible for standard lung cancer screening?\n\nResearchers will compare participants with lung cancer to participants without lung cancer to determine whether the urine VOC signature can identify differences between the two groups.\n\nParticipants will:\n\nProvide a urine sample for VOC testing. Provide health and demographic information as required by the study. Be classified into groups based on whether they have been diagnosed with lung cancer.",[93],"Lung Cancer",[95,96,97,98],"Smoking","Pulmonary Nodule","Lung Biopsy","Surgical Resection","2026-10-19",{"date":101,"type":102},"2026-10-22","ACTUAL",{"date":104,"type":102},"2026-09-15",{"date":106,"type":89},"2028-03-01",{"name":5,"class":6},1]