Ignite Creation Date:
2024-05-06 @ 8:17 PM
Last Modification Date:
2024-10-26 @ 3:24 PM
Study NCT ID:
NCT06320184
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-03-22
First Post:
2024-03-13
Brief Title:
AI for Lung Cancer Risk Definition in Computed Tomography Screening Programs
Sponsor:
Fondazione IRCCS Istituto Nazionale dei Tumori Milano
Organization:
Fondazione IRCCS Istituto Nazionale dei Tumori Milano
Study Overview
Official Title:
Artificial Intelligence Tools Integrating Blood Biomarkers and Radiomics to Define Lung Cancer Risk in Computed Tomography Screening Programs
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 biomarkers 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
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 biomarkers 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 smokings 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 trials 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
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 trials 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None