Artificial Intelligence in Lung Cancer Screening

Development of an Artificial Intelligence Model in Lung Cancer Screening for the Diagnosis of Lung Nodules and Risk Stratification in Subjects With Occupational and/or Smoking Exposure

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06444373

Acronym

INAIL BRIC

Enrollment

728

Registered

2024-06-05

Start date

2022-12-02

Completion date

2024-05-30

Last updated

2024-06-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Asbestos, Lung Cancer Screening

Keywords

Artificial Intelligence, Asbestos, Lung Cancer Screening

Brief summary

Single-center, non-profit, observational, retrospective study of collection of clinical and amnestic data and images to create, implement and develop a pilot model of an integrated virtual platform.

Detailed description

The project we propose is a study whose objective was to develop an artificial intelligence program integrated into a web-based platform for the optimization of the performance of lung cancer screening for the diagnosis of lung nodules and risk stratification in subjects exposed to environmental carcinogens and/or cigarette smoke. Inclusion criteria: Age > 50; smokers for at least 20 pack-years (20 cigarillos a day for 20 years) or former heavy smokers if they quit less than 15 years ago; and/or previous professional exposure to asbestos; absence of lung cancer symptoms; who performed lung cancer screening after the year 2000 upon approval of the study by the relevant EC.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

50 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

* Age > 50 years; * smokers for at least 20 pack-years (20 cigarettes a day for 20 years) or former heavy smokers if they quit less than 15 years ago; * and/or previous professional exposure to asbestos; * absence of lung cancer symptoms; * who performed lung cancer screening after the year 2000 upon approval of the study by the relevant Etical Committee

Exclusion criteria

* Age < 50 years * never smokers * lung cancer symptoms

Design outcomes

Primary

Measure	Time frame	Description
AIM 1 Pilot deep learning model	from enrollment to the end of treatment at 2 years	Development and fine-tuning of a pilot deep learning model for automatic detection and diagnosis of screen-detected nodules for risk stratification in subjects with asbestos exposure as part of a lung cancer screening program in high-risk subjects for exposure to asbestos and smoking on retrospective data.

Secondary

Measure	Time frame	Description
AIM 2 Clinical database	from enrollment to the end of treatment at 2 years	Development of an integrated system between the clinical database and several existing imaging volumetric software and risk models for the creation of a pilot platform in order to optimize the organizational management of lung cancer screening.

Countries

Italy

Outcome results