Understanding Lung Cancer Related Risk Factors and Their Impact

N/ANot Yet RecruitingNCT06473870

Biobizkaia Health Research Institute6,160 enrolled

Overview

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.

Lung cancer is the leading cause of cancer death worldwide, causing more deaths than breast and prostate cancer combined. The current five-year survival rate after diagnosis of all types of lung cancer in Europe is 13% (11.2% for men and 13.9% for women). The five-year survival rate for some types of lung cancer ranges from 6% to 7% (small cell LC) and 23% to 28% for non-small cell lung cancer (NSCLC). Currently there are important deficiencies when it comes to achieving an adequate lung cancer screening program. According to principles established in 1968, a screening program should be based on pathology that can be improved through the use of population screening. The evidence suggests two important gaps in early detection. On the one hand, the identification of risk factors beyond smoking and age. And on the other hand, the only tool for early detection that has been shown to reduce morbidity and mortality in lung cancer is chest CT, a test that may not be sustainable in the long term for many healthcare systems. In parallel, lung cancer diagnoses among never smokers and reduced smokers are increasing rapidly, suggesting that if lung cancer screening research continues focusing only on the heaviest smokers, a gap will persist between the population that performs the test and the population that suffers from the disease. Evidence also suggests that people undergoing screening are not being optimally referred for follow-up or kept engaged in long-term screening. Currently there are important deficiencies when it comes to achieving an adequate lung cancer screening program. The incidence in individuals without a history of smoking is increasingly higher. Therefore, an observational, longitudinal, multicenter cohort analytical study will be conducted to determine eligibility for screening based on individualized risk (based on age, a more detailed smoking history, occupational exposure, and other risk factors such as ethnicity and family history of lung cancer) and the development and validation of lung cancer risk predictive models that can improve screening efficiency and reduce lung cancer morbidity and mortality. These models will allow new clinical pathways and diagnostic workflow to be implemented to ensure rapid diagnosis and confirmation, including lung cancer subtype classification. The study consists of collecting data from participants in 4 visits over two years. During each visit, the clinical evaluation will be carried out, which will consist of the collection of sociodemographic data and clinical history, physical examination, concomitant medication, collection of exposure data and guide symptoms, Quality of Life questionnaires and geolocation. In addition, the following tests will be performed: low-dose computed tomography (LDCT), blood tests, genomic analysis and tests with new non-invasive devices (spectrometry on card (SPOC), breath analyzer (BAN) and broad-spectrum biomarker sensor patch (WBSP)). With all this, the aim is to develop and validate new tests based on new non-invasive and easy-to-use technologies that allow for the implementation of more efficient, acceptable and equitable population screening programs in the near future. The completion of this project will allow to provide data that can be used to better understand and discover new risk factors for suffering from lung cancer and therefore improve the management of the disease. Furthermore, this study will favor the reduction of long-term morbidity and mortality from lung cancer and will allow the future implementation of a lung cancer program.

Study Type

OBSERVATIONAL

Enrollment

6,160

Conditions

Lung Cancer Screening

Eligibility

Sex: ALLMin age: 40 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (5)

Centre Hospitalier Universitaire de Liège

Liège, Wallonia, Belgium

Riga East University Hospital

Riga, Latvia

Hospital Universitario Cruces

Barakaldo, Viscay, Spain

Hospital Universitario Virgen Macarena

Seville, Spain

Outcomes

Primary Outcomes

presence of pulmonary nodules

The main variable is the presence of pulmonary nodules identified by Low Dose Computerized Tomography (LDCT)

Time frame: 2 years

Lung Cancer diagnosis

The main variable is the presence of Lung Cancer diagnosis identified by Low Dose Computerized Tomography (LDCT).

Time frame: 2 years

Secondary Outcomes

Age

years

Time frame: 2 years

Gender

Male/female

Time frame: 2 years

Ethnicity

description of the ethnia

Time frame: 2 years

Socioeconomic factors

deprivation index

Time frame: 2 years

Education level

description of the education level

Time frame: 2 years

height

meter

Time frame: 2 years

weight

kilograms

Time frame: 2 years

Body Mass Index

kg/m\^2

Time frame: 2 years

Central Contacts

Eunate Arana-Arri, PhD

CONTACT

+34 944881593 eunate.aranaarri@osakidetza.eus

Jon E Idoyaga-Uribarrena, MPhar

CONTACT

+34 944881593 joneneko.idoyagauribarrena@bio-bizkaia.eus

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.