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.
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.
Study Type
OBSERVATIONAL
Enrollment
728
IRCCS San Raffaele Scientific Institute
Milan, Italy
AIM 1 Pilot deep learning model
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.
Time frame: from enrollment to the end of treatment at 2 years
AIM 2 Clinical database
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.
Time frame: from enrollment to the end of treatment at 2 years
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