Currently available screening programmes for lung cancer are limited by many challenges including low diagnostic accuracy, radiation exposure and high costs. New technologies in PET/CT scanners can allow cheaper and more sensitive exams with low radiation exposure. AI can be used to denoise LDCT to enhance the accuracy of imaging tests and build riskassessment models. This project aims to develop a new approach exploiting both these revolutionary advancements to bridge the existing gap in lung cancer screening. Patients in a high-risk population will be enrolled into two different cohorts undergoing LDCT scan and simultaneous \[18F\]FDG PET/CT on new-generation long axial field of view scanner (UO1) or screening with low LDCT only (UO2). AI will assist in image enhancement and interpretation and will develop a personalised risk-model guiding the following steps of clinical management, significantly improving early diagnosis of lung cancer, reducing mortality and healthcare costs.
Age > 50 years
PLCOm2012 risk prediction > 4%
Be willing to adhere to the study intervention through [18F]FDG PET/CT or LDCT imaging
Signed written informed consent form
Blood glucose levels >200 mg/dl,
Ongoing pregnancy and breastfeeding
Unwillingness to participate,
Previous diagnosis of lung cancer,
IRCCS San Raffaele
Lead sponsor
Fondazione Policlinico Universitario Campus Bio-Medico
Collaborator
University of Calabria
Collaborator
University of Salerno
Collaborator