How far have we come in our efforts to apply personalized therapy for lung cancer at bedside?

Background: Lung cancer, characterized by a 5-year survival rate and poor treatment efficiency, requires improvement in treatment efficiency through early detection and appropriate treatment. Predictive in vitro models for treatment responsiveness are crucial for lung cancer research. Traditional studies have utilized primary cells or cell lines of lung cancer; however, these approaches have limitations in reflecting heterogeneity in the body and implementing the 3-dimensional (3D) state of tumors in 2-dimensional cell culture without considering the tumor microenvironment. Animal experiments also have limitations owing to their high cost, long duration, and inability to perfectly mimic the human body. Objectives: Recently, efforts have been made to culture lung cancer tissues from patients in 3D as organoids and to recreate an in vivo environment using 3D bioprinting and microfluidic chips. Additionally, the introduction of machine learning has contributed to refining and advancing personalized lung cancer treatments. Conclusions: This review aims to introduce recent technologies employed for personalized lung cancer treatment.