Lung CancerNCT06320184
AI for Lung Cancer Risk Definition in Computed Tomography Screening Programs
An observational study of Lung Cancer and Blood Biomarkers, run by Fondazione IRCCS Istituto Nazionale dei Tumori, Milano at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer, Blood Biomarkers
- What is being tested?
- Artificial Intelligence risk model
- How many people
- 650 participants
- Where
- 1 siteItaly
- Study dates
- 30 Apr 2023to 30 Apr 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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).
Who may be able to join
- 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.
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Lung Cancer, Blood Biomarkers
- Being tested
- Artificial Intelligence risk model
- Keywords
- lung cancer, screening, microRNA, Low-dose computed tomography, Artificial Intelligence, Immune profiling
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence risk modelDiagnostic test
Combining blood-based biomarkers, radiologic parameters, clinical features, and AI tools to create a robust model to predict lung cancer risk.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Artificial Intelligence risk model | |
| Validation cohort | LDCT screening volunteers enrolled in the SMILE trial (clinicaltrial.gov NCT03654105) and in the RISP trial (clinicaltrial.gov NCT05766046). Receives: Diagnostic Test: Artificial Intelligence risk model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Aim 1
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.
Measured: 36 months
Secondary outcomes (2)
Aim 2
Evaluation of radiologic features and other LDCT markers related to respiratory and cardiovascular disorders.
Measured: 30 months
Aim 3
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).
Measured: 30 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2024
- Study start30 Apr 2023
- Primary completion30 Oct 2024
- Study completion30 Apr 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Fondazione IRCCS Istituto Nazionale dei Tumori, MilanoThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 650 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- University of Milano Bicocca
- Responsible party
- Principal investigator, Ugo Pastorino, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
- Investigators
- Ugo Pastorino, MD, Principal investigator, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06320184
- Sponsor's protocol ID
- INT 0083/23
- Status last verified
- Mar 2026
- First submitted
- 13 Mar 2024
- Last updated
- 27 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Tools Integrating Blood Biomarkers and Radiomics to Define Lung Cancer Risk in Computed Tomography Screening Programs
- ClinicalTrials.gov ID
- NCT06320184
- Acronym / sponsor ID
- INT 0083/23
- Conditions
- Lung Cancer, Blood Biomarkers
- Keywords
- lung cancer, screening, microRNA, Low-dose computed tomography, Artificial Intelligence, Immune profiling
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 650 participants (actual)
- Lead sponsor
- Fondazione IRCCS Istituto Nazionale dei Tumori, MilanoUniversity, hospital or other
- Collaborators
- University of Milano Bicocca
- Study start
- 30 Apr 2023 (actual)
- Primary completion
- 30 Oct 2024 (actual)
- Study completion
- 30 Apr 2026 (estimated)
- First posted
- 20 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention cohort | – | Diagnostic Test: Artificial Intelligence risk modelLDCT screening volunteers enrolled in the BioMILD trial (clinicaltrial.gov NCT02247453) with solid and sub-solid baseline LDCT lung nodules, including baseline-identified cancer patients. |
| Validation cohort | – | Diagnostic Test: Artificial Intelligence risk modelLDCT screening volunteers enrolled in the SMILE trial (clinicaltrial.gov NCT03654105) and in the RISP trial (clinicaltrial.gov NCT05766046). |
Who may qualify
- Ages
- 50 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence risk modelCombining blood-based biomarkers, radiologic parameters, clinical features, and AI tools to create a robust model to predict lung cancer risk. | Diagnostic test | Intervention cohort, Validation cohort |
Primary outcome
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.
2 secondary outcomes
Aim 2
Time frame: 30 months
Aim 3
Time frame: 30 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ugo Pastorino, MD principal investigator
Fondazione IRCCS Istituto Nazionale dei Tumori di Milano
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Blood Biomarkers, run by Fondazione IRCCS Istituto Nazionale dei Tumori, Milano at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 27 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Aim 1 (time frame: 36 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Mar 2026.