Observational Study That Will Analyse the Spread and Stratification of Lung Cancer Risk Using Artificial Intelligence

The main objective of the study is compare the performance of a Sybil AI tool with the LungRADS classifications assigned by radiologists for the risk stratification of pulmonary nodules. Furthermore, it aims to assess the correlation between the AI-predicted STAS and histopathological confirmation, alongside imaging and AI results. The hypothesis is that the Sybil AI model will demonstrate comparable predictive accuracy and that the features predicted by the AI will correlate with the presence of STAS.

Inclusion criteria

• Individuals of both sexes, smokers or former smokers for a maximum of 15 years;

• Smoking history of 20 pack-years or more;
• Between 50 and 80 years old;
• Provision of Free and Informed Consent in writing, signed and dated.

Exclusion criteria

• Individuals who are unable to undergo a CT scan;

• Individuals who cannot tolerate lying on their back for more than 10 minutes continuously;
• Individuals who present with symptoms highly suggestive of lung cancer (hemoptysis, chest pain, altered cough pattern, unintentional weight loss greater than 10 kg);
• Diagnosis of severe heart disease while using multiple medications;
• Diagnosis of severe lung disease, using multiple medications and/or requiring home oxygen therapy;
• History of radiation therapy to the chest area;
• Individuals undergoing cancer evaluation or treatment;
• Individuals exhibiting signs of respiratory distress (nasal flaring, suprasternal retraction, use of accessory muscles, cyanosis);
• Pregnancy.