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AI for Lung Cancer Risk Definition in Computed Tomography Screening Programs

NCT06320184 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 650

Last updated 2026-03-27

No results posted yet for this study

Summary

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

Interventions

DIAGNOSTIC_TEST

Artificial Intelligence risk model

Combining blood-based biomarkers, radiologic parameters, clinical features, and AI tools to create a robust model to predict lung cancer risk.

Sponsors & Collaborators

  • University of Milano Bicocca

    collaborator OTHER

  • Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

    lead OTHER

Principal Investigators

  • Ugo Pastorino, MD · Fondazione IRCCS Istituto Nazionale dei Tumori di Milano

Eligibility

Min Age
50 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-04-30
Primary Completion
2024-10-30
Completion
2026-04-30

Countries

  • Italy

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06320184 on ClinicalTrials.gov