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

ARtificial Intelligence for heAlth and Prevention of Smoking-related Diseases

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

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence (AI); Lung Cancer Screening Program
• Intervention / Treatment: Diagnostic test: Computed tomography (CT) scan low dose; Procedure: Blood sampling; Other: Spirometry; Other: Questionnaires; Other: Smoking cessation program; Other: Carbon monoxide measurment; Other: Cardiovascular primary prevention; Procedure: Tissue sampling (lung)

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 Type: Interventional
• Enrollment (Estimated): 2840
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Piergiorgio Muriana, MD; Phone Number: +39 0226437232; Email: muriana.piergiorgio@hsr.it

Study Locations

• Italy
  • Milan, Italy, 20132
    • Recruiting
    • Scientific Institute Ospedale San Raffaele
    • Contact: Piergiorgio Muriana, MD; Phone Number: +39 0226437232; Email: muriana.piergiorgio@hsr.it
    • Principal Investigator: Piergiorgio Muriana, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

High-risk screening subjects

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 Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

4

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: High-risk screening subjects; Ever and former male and female smokers aged over 50 years old at high risk for lung cancer, cardiovascular diseases and chronic obstructive pulmonary disease (COPD). Additional procedures for this group: Spirometry; Nurse evaluation; Epidemiological questionnaire, Quality of Life (QoL) questionnaires; Smoking cessation program; Cardiovascular primary prevention; measurement of carbon monoxide (CO); Blood storage; Computed tomography (CT) scan low-dose	Diagnostic test: Computed tomography (CT) scan low dose; The radiological investigation will be done with multi-detector-row (64 or more) computed tomography (CT) scanners at low-dose protocol. The low-dose spiral CT consists of a CT study of the chest, without the need for injection of contrast medium, characterized by less radio exposure than the standard CT of the chest with high sensitivity in detecting pulmonary nodules.; Procedure: Blood sampling; Peripheral venous blood sampling (20 ml); Other: Spirometry; Spirometry measurement using spirometer; Other: Questionnaires; Compilation of epidemiological questionnaire, quality of life questionnaires; Other: Smoking cessation program; Study guarantee valid support for quitting smoking, which for a smoker is a more effective intervention to reduce the risk of developing lung cancer, myocardial infarction and other smoking-related diseases; Other: Carbon monoxide measurment; Measurment of Carbon monoxide (CO); Other: Cardiovascular primary prevention; Intervention done in order to find the presence of coronary calcifications
Experimental: Previous positive high-risk screening subjects; Subjects enrolled in previous screening cohorts in this Institute with the presence of lung nodules >4 mm. Additional procedure for this group: Computed tomography (CT) scan low-dose	Diagnostic test: Computed tomography (CT) scan low dose; The radiological investigation will be done with multi-detector-row (64 or more) computed tomography (CT) scanners at low-dose protocol. The low-dose spiral CT consists of a CT study of the chest, without the need for injection of contrast medium, characterized by less radio exposure than the standard CT of the chest with high sensitivity in detecting pulmonary nodules.; Other: Smoking cessation program; Study guarantee valid support for quitting smoking, which for a smoker is a more effective intervention to reduce the risk of developing lung cancer, myocardial infarction and other smoking-related diseases
No Intervention: Previous negative high-risk screening subjects; Subjects enrolled in previous screening cohorts in this Institute with negative computed tomography (CT) scan. No additional procedures for this group.
Experimental: Lung cancer patients; Lung cancer patients diagnosed outside screening and treated at San Raffaele Hospital. Additional procedures for this group: biobank tissue and blood storage.	Procedure: Blood sampling; Peripheral venous blood sampling (20 ml); Procedure: Tissue sampling (lung); sampling of tumor and healthy tissue during surgery

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Creation of Artificial Intelligence (AI) algorithm	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.	from enrollment to 48 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Multimodal program	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	from enrollment to 48 months

Collaborators and Investigators

• Sponsor: Scientific Institute San Raffaele

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2024
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): November 1, 2028

Study Registration Dates

• First Submitted: October 1, 2024
• First Submitted That Met QC Criteria: October 1, 2024
• First Posted (Actual): October 3, 2024

Study Record Updates

• Last Update Posted (Actual): October 3, 2024
• Last Update Submitted That Met QC Criteria: October 1, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Keywords: Lung cancer; Artificial Intelligence; CT scan low-dose; High-risk sucjects
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Physiological Effects of Drugs; Neurotransmitter Agents; Molecular Mechanisms of Pharmacological Action; Antimetabolites; Gasotransmitters; Carbon Monoxide
• Other Study ID Numbers: CET 288-2024

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No