[Utility of Imaging Mass Cytometry in Spatial Analysis of Non-Small Cell Lung Cancer]

Immune checkpoint inhibitors have improved substantially clinical outcomes in patients with non-small cell lung cancer (NSCLC). However, considerable variability in therapeutic response persists even among tumors with the same histologic subtype or PD-L1 expression status. Recent studies have demonstrated that this heterogeneity is closely associated with the spatial architecture of the tumor microenvironment, including the localization and interactions of tumor cells, T cells, dendritic cells, macrophages, and fibroblasts. Imaging mass cytometry (IMC) combines metal isotope-labeled antibo dies, laser ablation, and time-of-flight mass spectrometry. This enables simultaneous analysis of multiple proteins in formalin-fixed paraffin-embedded tissue sections at single-cell resolution. Critically, spatial information is preserved throughout the process. By integrating artificial intelligence-driven image analysis, IMC enables the evaluation of cellular density and phenotype. It also enables the quantitative analysis of intercellular distance and cellular proximity. In NSCLC, intratumoral infiltration of CD8-positive T cells, spatial distribution of myeloid cells, maturation of tertiary lymphoid structures, and stromal barrier formation have all been associated with prognosis and response to immunotherapy. Furthermore, integration with artificial intelligence-based digital pathology has enabled the identification of complex spatial patterns that are difficult to detect using conventional histopathologic assessment alone. This review summarizes the principles and analytical concepts of IMC and discusses recent advances in spatial analysis in NSCLC, as well as its potential clinical applications.