Development of an Artificial Intelligence Model for the Identification and Prevention of Smoking-related Diseases.

Not Applicable

Recruiting

• Conditions: Lung Cancer Screening Program; Artificial Intelligence (AI)

• Registration Number: NCT06626178
• Lead Sponsor: Scientific Institute San Raffaele

Brief Summary

The study is an interventional pilot study. The study is designed to be monocentric and it presents additional procedues.

Detailed Description

Interventional pilot study, single-center with additional procedures, such as completion of EORTC-QLQ-LC29, EORTC-QLQ-C30 questionnaires, motivational test, Fagestrom test, anamnestic questionnaire, spirometry, measurement of carbon monoxide, Low-dose spiral computed tomography without contrast medium, peripheral venous blood sampling for a volume of 20 ml.

The study has the main objective of traininig and validate a reliable and unbiased Artificial Intelligence (AI) algorithm that detects the presence of nodules and differentiates between malignant or benign tumor types.

The study considers patients with suspected diagnosis or with a dignosis of lung cancer, smokers and former smokers over 50 years of age at high risk of lung cancer and subjects enrolled in previous screening cohorts at this Institute.

Study Timeline

• Status Verified: 2024-10
• Study Start: 2024-10-01
• Study First Submitted: 2024-10-01
• Study First Submitted QC: 2024-10-01
• Last Update Submitted: 2024-10-01
• Study First Posted: 2024-10-03
• Last Update Posted: 2024-10-03
• Primary Completion: 2026-11-01
• Study Completion: 2028-11-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2840

Inclusion Criteria

• Age >= 50 years old
• Active smokers
• Former smokers (from no more than 15 years)
• Pack/year >20
• Risk-prediction model from Prostate, Lung, Colorectal, and Ovarian study (PLCOm2012) >1.2%
• Provision and signature of informed consent

Exclusion Criteria

• Previous or concurrent neoplastic disease, excluding skin cancers
• Cognitive or other problems that could hinder the collection of informed consent
• Severe pulmonary or extra pulmonary disease
• Previous low-dose computed tomography (CT) scan in the past 12 months

Previous high-risk positive screening subjects

Inclusion Criteria:

• Subjects enrolled in previous lung cancer screening with the presence of lung nodules >4 mm and candidate to additional computed tomography (CT)
• Signed informed consent

Exclusion Criteria:

• None

Previous high-risk negative screening subjects

Inclusion Criteria:

• Subjects enrolled in previous lung cancer screening in this Institute with negative computed tomography (CT)
• Signed informed consent

Exclusion Criteria:

• None

Lung Cancer patients

Inclusion Criteria:

• Patients with diagnosis or suspicious diagnosis of lung cancer candidate to surgical treatment or already submitted to it
• Patients with diagnosis of lung cancer treated with surgical resection
• Signed informed consent

Exclusion Criteria:

• computed tomography (CT) scans not available at San Raffaele Hospital
• Previous neoadjuvant treatment

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Creation of Artificial Intelligence (AI) algorithm	from enrollment to 48 months	To train and validate a reliable and unbiased Artificial Intelligence (AI) algorithm that detects the presence of nodules and differentiates between malignant or benign tumor types.; AUC (Area Under the Curve) values, expressed as mean and standard deviation (SD), comparing the ability in detecting the presence of nodules and differentiating the malignancy or benignity of a radiologist versus an AI algorithm, both trained on the same patient group.

Secondary Outcome Measures

Name	Time	Method
Multimodal program	from enrollment to 48 months	Develop a multimodal program to enhance the prevention and the early detection of multiple smoking-related diseases Presence and absence of lung nodules \> 4 mm with computed tomography (CT) scan

Trial Locations

Locations (1)

Scientific Institute Ospedale San Raffaele; 🇮🇹 Milan, Italy