Glioblastoma is a highly invasive primary malignant brain tumor, and its precise intraoperative identification has always been a major challenge in clinical practice. In this study, we propose a label-free and multiplexed strategy for glioblastoma identification based on two-photon fluorescence lifetime imaging and a robust quantitative analysis. Specifically, we constructed an orthotopic xenograft model of human glioblastoma and then performed fresh tissue imaging. From the acquired images, we performed a gray-level co-occurrence matrix texture analysis and phasor fluorescence lifetime analysis to quantify the microstructural difference and metabolic characteristics of tumor and peritumor areas. Based on the above analysis, we extracted 4 morphological texture parameters and 4 metabolic phasor parameters with potential as identification indicators for glioblastoma. We have integrated the novel multi-parameter analysis strategy to achieve robust and effective discrimination between glioblastoma and peritumoral tissue. This advancement and the method presented in this study will allow for the achievement of real-time, label-free, and multi-dimensional precise glioblastoma identification in the future.
Wang et al. (Fri,) studied this question.