From Lung Cancer Predictive Models to MULTIPREVENTion

The early diagnosis and treatment of civilizational diseases remain a significant challenge worldwide. Although advances in medical technology have led to the introduction of more screening options over time, these measures are still insufficient to effectively reduce mortality from deadly diseases such as lung cancer (LC), cardiovascular diseases (CVD), diabetes, and chronic obstructive pulmonary disease (COPD). These conditions pose a major public health burden, underlying the urgent need for more comprehensive and efficient prevention strategies. Recently, the concept of 'multiscreening' has emerged as a promising approach. Multiscreening involves the simultaneous screening for multiple diseases using integrated diagnostic methods, potentially improving early detection rates and optimizing resource utilization. In 2024, Rzyman W. et al. launched the MULTIPREVENT epidemiological study, which aims to develop and validate a low-dose computed tomography (LDCT)-based screening test for civilizational diseases. This study represents a step forward in the pursuit of more effective, minimally invasive diagnostic tools that could facilitate earlier intervention and improve patient outcomes. To better understand the potential of multiscreening approaches and their clinical utility, it is essential to evaluate the existing predictive models used for identifying individuals at high risk for these diseases. This narrative review focuses primarily on lung cancer risk prediction models used in LDCT screening while situating these approaches within the broader conceptual framework of the MULTIPREVENT project, aimed at future integration of multi-disease prevention strategies. With this analysis, we aim to provide insights that will guide the development of more accurate, integrative screening tools that could reduce the global burden of these diseases.