Biomarker discovery using multimodal data with the potential application in lung tumor diagnosis

Lung cancer is one of the leading causes of cancer-related death in humans, with very high morbidity and mortality. Accurately identifying the lung tumor markers can significantly improve the diagnostic capability of lung cancer, which helps clinicians detect potential lung cancer lesions earlier and take a more timely and effective treatment regime, thus improving the survival rate and quality of life of cancer patients. Recently, there has been growing interest in biomarker discovery based on multi-omics and multimodal data, which explore high-dimensional, high-throughput, and multi-scale biomedical data of lung cancer patients from various perspectives, including molecular, histopathology, radiology, and clinical records. Additionally, machine learning (ML) techniques can handle complex multi-omics and multimodal data more efficiently than traditional biomarker discovery methods, so ML has been widely adopted in finding lung cancer biomarkers. This paper reviews biomarker discovery using multimodal data with the potential application in lung tumor diagnosis. Specifically, we comprehensively summarize and analyze the current research status of biomarker discovery from three aspects, including multimodal data feature extraction and selection, multimodal integration methods, and biomarker discovery methods. Furthermore, we highlight some of the limitations of existing machine learning-based biomarker discovery methods using multi-omics and multimodal data for lung tumor diagnosis and outline our future directions.