Figure 1.

Applications of ML model in lung cancer

We presented an overview of ML methodologies for different aspects of lung cancer therapies, including CAD from imaging datasets, lung cancer early detection based on sequencing technologies, data integration and biomarker extraction from multi-omics datasets, treatment response and prognosis prediction, and immunotherapy studies. ML, machine learning; IC50, half-maximal inhibitory concentration; HLA, human leukocyte antigen; CT, computed tomography; MALDI, matrix-assisted laser desorption/ionization; DL, deep learning; cfDNA, cell-free DNA; CAD, computer-aided diagnosis; CNV, copy number variation; RECIST, Response Evaluation Criteria in Solid Tumors; TIL, tumor-infiltrating lymphocyte.