Clinical trial · Observational
AI for Lung Cancer Risk Definition in Computed Tomography Screening Programs
Artificial Intelligence Tools Integrating Blood Biomarkers and Radiomics to Define Lung Cancer Risk in Computed Tomography Screening Programs
- 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)
Low-dose computed tomography (LDCT) lung cancer (LC) screening can reduce mortality among heavy smokers, but there is a critical need to better identify people at higher risk and to reduce harms related to management of benign nodules. The most promising strategy is to combine novel tools to optimize clinical decisions and increase the benefit of screening. In this respect, the investigators already demonstrated that the combination of baseline LDCT features with a minimal invasive microRNA blood test was able to more precisely estimate the individual risk of developing LC. The investigators posit that additional immune-related and radiologic features can be integrated with the help of artificial intelligence (AI) to further implement LDCT screening strategies. The project will answer whether the combination of (bio)markers of different origin can predict LC development at baseline and over time, indicate which screen-detected lung nodules are likely to be malignant and ultimately reduce LC and all cause mortality.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Blood Biomarkers | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Artificial Intelligence risk model | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Intervention cohort
- description
- LDCT screening volunteers enrolled in the BioMILD trial (clinicaltrial.gov NCT02247453) with solid and sub-solid baseline LDCT lung nodules, including baseline-identified cancer patients.
- interventionNames
- Diagnostic Test: Artificial Intelligence risk model
- label
- Validation cohort
- description
- LDCT screening volunteers enrolled in the SMILE trial (clinicaltrial.gov NCT03654105) and in the RISP trial (clinicaltrial.gov NCT05766046).
- interventionNames
- Diagnostic Test: Artificial Intelligence risk model
Primary outcomes (1)
- measure
- Aim 1
- timeFrame
- 36 months
- description
- Development of a risk classifier using AI tools based on combination of blood biomarkers, imaging and clinical data to improve LDCT screening sensitivity and positive predictive value.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 50 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * current heavy smokers of ≥ 30 pack/years or former smokers with the same smoking habits having stopped from 10 years or less; * current heavy smokers of ≥ 20 pack/years or former smokers with the same smoking habits having stopped from 10 years or less with additional risk factors such as family history of lung cancer, prior diagnosis of chronic obstructive pulmonary disease (COPD) or pneumonia; * Suspected solid and sub-solid LDCT lung nodules. Exclusion Criteria: \-
References
Publications (0)
Data not yet available