AI for Lung Cancer Risk Definition in Computed Tomography Screening Programs

March 26, 2026 updated by: Ugo Pastorino, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

Artificial Intelligence Tools Integrating Blood Biomarkers and Radiomics to Define Lung Cancer Risk in Computed Tomography Screening Programs

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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Active, not recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

Detailed Description

Lung cancer constitutes 28% of all cancer deaths in Europe, with 70% of patients diagnosed at advanced stages and a mere 21% 5-year survival rate. Despite smoking's causative link to almost 90% of cases, global smoking rates persist, posing a long-term public health challenge. Our focus lies in refining lung cancer risk assessment using blood-based biomarkers, particularly circulating microRNAs (miRNAs) and C-reactive protein. Biennial LDCT screenings and blood tests predicting lung cancer risk have shown effectiveness, as seen in our pioneering work within the BioMILD trial since 2013.

The BioMILD trial, encompassing 4119 volunteers, combines LDCT and microRNA biomarkers, demonstrating feasibility and safety over 4 years. Our current endeavor aims to develop a predictive model for LDCT-detected high-risk lung nodules, incorporating blood, functional, and radiomics biomarkers. Leveraging the BioMILD trial's biorepository, imaging database, and 20 patient-derived xenografts (PDXs), the investigators utilize advanced artificial intelligence (AI) tools for comprehensive analysis. This approach, involving 400 subjects with solid and sub-solid LDCT lung nodules, including 100 baseline-identified cancer patients, is crucial.

By combining blood-based biomarkers, radiologic parameters, clinical features, and AI tools, the investigators aim to create a robust model. This model will be validated using an independent set of 100 subjects (25 with and 75 without lung cancer) from the ongoing SMILE screening trial. If successful, our vision is to prospectively implement this panel in clinical contexts where it proves beneficial. Our mission is to reduce lung cancer mortality, optimizing screening interventions with novel, non-invasive tools for all high-risk individuals while minimizing costs and radiation exposure-related harms.

Aim 1 Assessment of an Immune Signature Classifier (ISC) on peripheral blood mononuclear cell (PBMC) samples collected from screen detected solid and sub-solid LDCT lung nodules and integration of ISC with existing biomarkers such as the MSC test and the c-Reactive Protein (cRP).

Aim 2 Evaluation of radiologic features and other LDCT markers related to respiratory and cardiovascular disorders.

Aim 3 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.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Actual)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
650

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Italy
      • Milan, Italy, 20133
        • Fondazione IRCCS Istituto Nazionale dei Tumori

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

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, in the SMILE trial (clinicaltrial.gov NCT03654105) and in the RISP trial (clinicaltrial.gov NCT05766046).

Description

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:

-

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.
Intervention cohort
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.
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.
Validation cohort
LDCT screening volunteers enrolled in the SMILE trial (clinicaltrial.gov NCT03654105) and in the RISP trial (clinicaltrial.gov NCT05766046).
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.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Aim 1
Time Frame: 36 months
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.
36 months

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Aim 2
Time Frame: 30 months
Evaluation of radiologic features and other LDCT markers related to respiratory and cardiovascular disorders.
30 months
Aim 3
Time Frame: 30 months
Assessment of an Immune Signature Classifier (ISC) on peripheral blood mononuclear cell (PBMC) samples collected from screen detected solid and sub-solid LDCT lung nodules and integration of ISC with existing biomarkers such as the MSC test and the c-Reactive Protein (cRP).
30 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

Collaborators

Collaborators

An organization other than the sponsor that provides support for a clinical study. This support may include activities related to funding, design, implementation, data analysis, or reporting.
University of Milano Bicocca

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Principal Investigator: Ugo Pastorino, MD, Fondazione IRCCS Istituto Nazionale dei tumori di Milano

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
April 30, 2023

Primary Completion (Actual)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
October 30, 2024

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
April 30, 2026

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
March 13, 2024

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
March 13, 2024

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
March 20, 2024

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
March 27, 2026

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
March 26, 2026

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
March 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • INT 0083/23

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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