  Due to its predominantly asymptomatic or mildly symptomatic progression, lung
cancer is often diagnosed in advanced stages, resulting in poorer survival
rates for patients. As with other cancers, early detection significantly
improves the chances of successful treatment. Early diagnosis can be
facilitated through screening programs designed to detect lung tissue tumors
when they are still small, typically around 3mm in size. However, the analysis
of extensive screening program data is hampered by limited access to medical
experts. In this study, we developed a procedure for identifying potential
malignant neoplastic lesions within lung parenchyma. The system leverages
machine learning (ML) techniques applied to two types of measurements: low-dose
Computed Tomography-based radiomics and metabolomics. Using data from two
Polish screening programs, two ML algorithms were tested, along with various
integration methods, to create a final model that combines both modalities to
support lung cancer screening.
