Klin Onkol 2024; 37(6): 408-418. DOI: 10.48095/ccko2024408.
Background: Lung cancer is one of the leading causes of death worldwide, with incidence and mortality significantly affected by population ageing and changes in the prevalence of risk factors. Lung nodules, which are often detected incidentally on imaging studies, pose a significant diagnostic challenge as they may indicate both benign and malignant processes. Correct diagnosis and management of these nodules is therefore essential to optimize clinical outcomes. Purpose: This article provides a comprehensive review of diagnostic and therapeutic approaches to pulmonary nodules, focusing on the assessment of malignant potential based on nodule morphology, size and growth potential. Risk factors influencing the decision-making process such as smoking, age and exposure to carcinogens are also discussed. In addition, key recommendations from the Fleischner Society and the British Thoracic Society are discussed in detail. The article analyses the benefits of modern imaging techniques, including the use of artificial intelligence (AI) in the diagnosis of lung nodules. AI technologies, particularly deep learning techniques, have shown high accuracy in detecting and assessing malignancy risk, and their use is increasingly complementary to expert clinical judgement. Finally, the article highlights the importance of a multidisciplinary approach to the diagnosis and management of lung nodules, and also mentions the implementation of a pilot lung cancer screening programme in the Czech Republic aimed at early detection of the disease. This programme has the potential to significantly reduce lung cancer mortality and improve patient prognosis.
