From Lung Cancer Predictive Models to MULTIPREVENTion

Area of Science:

• Epidemiology
• Public Health
• Medical Diagnostics

Background:

• Civilizational diseases like lung cancer (LC), cardiovascular diseases (CVD), diabetes, and COPD pose significant global health challenges.
• Current screening methods are insufficient for effective mortality reduction.
• Multiscreening, screening for multiple diseases simultaneously, is an emerging strategy to improve early detection and resource use.

Purpose of the Study:

• To review lung cancer risk prediction models used in low-dose computed tomography (LDCT) screening.
• To situate these models within the MULTIPREVENT project's framework for multi-disease prevention.
• To provide insights for developing accurate, integrative screening tools.

Main Methods:

• Narrative review of existing risk prediction models for lung cancer in LDCT screening.
• Analysis within the context of the MULTIPREVENT epidemiological study.
• Evaluation of potential for integrating multi-disease screening strategies.

Main Results:

• Existing lung cancer risk prediction models are a focus for LDCT screening.
• The MULTIPREVENT study aims to validate an LDCT-based screening test for civilizational diseases.
• Further development is needed for comprehensive, multi-disease screening tools.

Conclusions:

• Early diagnosis and prevention of civilizational diseases require more effective strategies.
• Multiscreening, particularly using LDCT, holds potential for improving early detection rates.
• Integrating risk prediction models is crucial for advancing multi-disease prevention and reducing global health burdens.