Clinical trial · Observational
Understanding Lung Cancer Related Risk Factors and Their Impact
NCT06473870CI-TRIAL-00078227LUCIAnot yet recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
LUCIA aims to develop prediction models for the early diagnosis of lung cancer based on the identification of risk factors and deeper cellular knowledge, by recording real-world data; with risk assessment tools, non-invasive devices and omics analysis. These models will enable new clinical pathways and diagnostic workflow to be implemented to ensure early diagnosis and confirmation, including classification of lung cancer subtype.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer Screening | Lung Neoplasm | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (1)
- label
- Study population
- description
- Adult subjects (40 years old or higher), smokers and non-smokers, both women and men who have the capacity to comply with the study follow-up and sign the informed consent, will be recruited from "Servicio Andaluz de Salud" (SAS), "Osakidetza Servicio Vasco de Salud" (OSA), "Centre Hospitalier Universitaire de Liège" (CHUL) and "Centre for Tuberculosis and Lung Diseases (CTLD) of Riga East University Hospital (REUH)".
Primary outcomes (2)
- measure
- presence of pulmonary nodules
- timeFrame
- 2 years
- description
- The main variable is the presence of pulmonary nodules identified by Low Dose Computerized Tomography (LDCT)
- measure
- Lung Cancer diagnosis
- timeFrame
- 2 years
- description
- The main variable is the presence of Lung Cancer diagnosis identified by Low Dose Computerized Tomography (LDCT).
Secondary outcomes (65)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 40 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria (for the 3 phases): * Subjects aged between 40 and 80 years * Both genders, of which at least 37% must be women to ensure representativeness * Willingness and ability to comply with scheduled visits, laboratory tests, and other trial procedures * Written informed consent obtained prior to performing any protocol-related procedures. Inclusion criteria for Phase 2: Precision Screening: * High risk of developing Lung Cancer volunteers will be selected by Lung Cancer risk factors modelling. Inclusion criteria for Phase 3: Diagnosis: \- Patients diagnosed with indeterminate pulmonary nodules or Lung Cancer from the screening phases. Exclusion Criteria: * Subjects under 40 years of age * Unable to be followed-up for at least 2-years or complete the study * Subjects that do not sign the informed consent * Current or prior history of lung cancer * History of neoplasia in the previous 5 years except non-melanoma skin cancer * Moderate-severe comorbidities that prevent completion of a diagnostic study in the event of findings suggestive of lung neoplasia (by means of the investigator's clinical judgment) or surgical intervention (\< 6 months) if not previously confirmed by cytohistology. * Vulnerable subjects: severe psychiatric comorbidity, adults under guardianship or deprived of liberty * Pregnant women
References
Publications (26)
- BACKGROUNDvan Meerbeeck JP, Franck C. Lung cancer screening in Europe: where are we in 2021? Transl Lung Cancer Res. 2021 May;10(5):2407-2417. doi: 10.21037/tlcr-20-890. PMID 34164288
- BACKGROUNDOudkerk M, Liu S, Heuvelmans MA, Walter JE, Field JK. Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol. 2021 Mar;18(3):135-151. doi: 10.1038/s41571-020-00432-6. Epub 2020 Oct 12. PMID 33046839
- BACKGROUNDAdams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet. 2023 Feb 4;401(10374):390-408. doi: 10.1016/S0140-6736(22)01694-4. Epub 2022 Dec 20. PMID 36563698
- BACKGROUNDGomez-Carballo N, Fernandez-Soberon S, Rejas-Gutierrez J. Cost-effectiveness analysis of a lung cancer screening programme in Spain. Eur J Cancer Prev. 2022 May 1;31(3):235-244. doi: 10.1097/CEJ.0000000000000700. PMID 34406177
- BACKGROUNDThandra KC, Barsouk A, Saginala K, Aluru JS, Barsouk A. Epidemiology of lung cancer. Contemp Oncol (Pozn). 2021;25(1):45-52. doi: 10.5114/wo.2021.103829. Epub 2021 Feb 23. PMID 33911981
- BACKGROUNDSchabath MB, Cote ML. Cancer Progress and Priorities: Lung Cancer. Cancer Epidemiol Biomarkers Prev. 2019 Oct;28(10):1563-1579. doi: 10.1158/1055-9965.EPI-19-0221. PMID 31575553
- BACKGROUNDZhang T, Joubert P, Ansari-Pour N, Zhao W, Hoang PH, Lokanga R, Moye AL, Rosenbaum J, Gonzalez-Perez A, Martinez-Jimenez F, Castro A, Muscarella LA, Hofman P, Consonni D, Pesatori AC, Kebede M, Li M, Gould Rothberg BE, Peneva I, Schabath MB, Poeta ML, Costantini M, Hirsch D, Heselmeyer-Haddad K, Hutchinson A, Olanich M, Lawrence SM, Lenz P, Duggan M, Bhawsar PMS, Sang J, Kim J, Mendoza L, Saini N, Klimczak LJ, Islam SMA, Otlu B, Khandekar A, Cole N, Stewart DR, Choi J, Brown KM, Caporaso NE, Wilson SH, Pommier Y, Lan Q, Rothman N, Almeida JS, Carter H, Ried T, Kim CF, Lopez-Bigas N, Garcia-Closas M, Shi J, Bosse Y, Zhu B, Gordenin DA, Alexandrov LB, Chanock SJ, Wedge DC, Landi MT. Genomic and evolutionary classification of lung cancer in never smokers. Nat Genet. 2021 Sep;53(9):1348-1359. doi: 10.1038/s41588-021-00920-0. Epub 2021 Sep 6.