Application of fluorescence lifetime imaging-integrated deep learning analysis for cancer research

Fluorescence lifetime imaging microscopy (FLIM) has emerged as a transformative imaging technique in cancer research, offering quantitative insights into cellular metabolism, tumor microenvironments, and therapeutic responses. By measuring the fluorescence lifetimes of metabolic cofactors such as NADH and FAD, FLIM facilitates the analysis of cancer-specific metabolic reprogramming and heterogeneity. Integration with deep learning further enhances FLIM’s diagnostic and therapeutic potential, enabling high-resolution imaging, automated data analysis, and biomarker identification. This review provides a comprehensive overview of the principles and technological advancements of FLIM, highlighting its applications in cancer diagnostics, drug delivery, and therapy, as well as its integration with deep learning to increase imaging precision and data interpretation. Challenges such as high costs, high computational complexity, and the need for standardized imaging protocols are also addressed. By bridging FLIM with cutting-edge computational techniques, this review highlights its potential to revolutionize cancer research, paving the way for early diagnosis, personalized therapies, and deeper insights into tumor biology.