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,
Parallel
LDCT scan and simultaneous [18F]FDG PET/CT on new-generation long axial field of view scanner
Procedure/Surgery18F\]FDG will be injected intravenously, and PET/CT images will be acquired after 60 minutes (± 10 minutes). PET/CT images will be first analysed with the lung window to detect any findings suggestive of a lung tumour. Whole-body PET/CT images will be then analysed to detect any incidental finding in the chest as well as other anatomical areas included in the field of view. PET/CT images will be considered positive if there is at least one non-calcified lung nodule or any suspicious finding on CT scan characterised by focal \[18F\]FDG uptake deviating from physiological distribution or above physiological background activity.
LDTC only
Procedure/SurgeryThe LDCT scan will be performed in single deep inspiratory breath hold. No intravenous contrast will be administered.
The primary objective is to assess the diagnostic accuracy of low-dose [18F]FDG PET/CT for screening in high-risk individuals or lung cancer Time Frame:
The primary endpoint is the detection rate of \[18F\]FDG PET/CT for suspicious lung nodules. The sensitivity, specificity and accuracy will be calculated by a per-patient and per-lesion analysis based on the clinico-radiological follow-up. PET/CT images will be considered positive if there is at least one non-calcified lung nodule or one suspicious finding on CT scan characterised by focal \[18F\]FDG uptake that deviates from the physiological distribution or is above the physiological background activity. If there are no areas of increased uptake compared to physiological background activity, PET/CT images are considered negative.
Assessment of the diagnostic accuracy of LDCT alone for screening in high-risk individuals or lung cancer and accuracy of [18F]FDG PET/CT for the additional detection of non-lung cancers in a high-risk population.
The sensitivity, specificity and accuracy will be calculated by a per-patient and per-lesion analysis based on the clinico-radiological follow-up. LDCT will be defined as positive if at least one non-calcified lung nodule \>5 mm in any diameter is detected. LDCT will be defined as negative if no clinically significant morphological alterations are detected. Lung-RADS criteria will be used to classify detected lung nodules. PET/CT images will be considered positive if there is at least one suspicious morphological finding on CT scan characterised by focal \[18F\]FDG uptake that deviates from the physiological distribution or is above the physiological background activity. If there are no areas of increased uptake compared to physiological background activity, PET/CT images are considered negative.
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IRCCS San Raffaele
Lead sponsor
Fondazione Policlinico Universitario Campus Bio-Medico
Collaborator
University of Calabria
Collaborator
University of Salerno
Collaborator
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