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Due to its predominantly asymptomatic or mildly symptomatic progression, lungcancer is often diagnosed in advanced stages, resulting in poorer survivalrates for patients. As with other cancers, early detection significantlyimproves the chances of successful treatment. Early diagnosis can befacilitated through screening programs designed to detect lung tissue tumorswhen they are still small, typically around 3mm in size. However, the analysisof extensive screening program data is hampered by limited access to medicalexperts. In this study, we developed a procedure for identifying potentialmalignant neoplastic lesions within lung parenchyma. The system leveragesmachine learning (ML) techniques applied to two types of measurements: low-doseComputed Tomography-based radiomics and metabolomics. Using data from twoPolish screening programs, two ML algorithms were tested, along with variousintegration methods, to create a final model that combines both modalities tosupport lung cancer screening.

Combining low-dose CT-based radiomics and metabolomics for early lung cancer screening support