Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

Abstract

IntroductionThe intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence.MethodsWith this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured.ResultsIn healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum.DiscussionIn conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

Intraoperative Imaging and Optical Visualization Techniques for Brain Tumor Resection: A Narrative Review

“…Finally, the future of intraoperative imaging in neurosurgical oncology is guided by the synergistic incorporation of AI and ML [127,128,136]. The capability of these systems to refine the delineation of tumor and healthy tissues is pivotal to the success of tumor diagnostics and surgical resections, promising enhancements in both surgical precision and patient outcomes [128,137]. An exemplar of this synergy is the integration of AI with FGS, a vital technology in differentiating tumor tissue during brain tumor surgery [137,138].…”

“…The capability of these systems to refine the delineation of tumor and healthy tissues is pivotal to the success of tumor diagnostics and surgical resections, promising enhancements in both surgical precision and patient outcomes [128,137]. An exemplar of this synergy is the integration of AI with FGS, a vital technology in differentiating tumor tissue during brain tumor surgery [137,138]. Multiple studies illustrated the utility of AI for real-time intraoperative cytological diagnosis of CNS tumors and training a deep learning model on a variety of brain lesions [127,128,136].…”

Optical Differentiation of Brain Tumors Based on Raman Spectroscopy and Cluster Analysis Methods

“…Recently, several research groups have also focused their interest on the combination of different optical-spectral methods. For example, in the most recent study, a combination of histology based on stimulated Raman scattering and analysis of an autofluorescence signal with two-photon excitation was considered [19]. The authors obtained interesting results on the correspondence between autofluorescence signatures of tissues and their pathomorphological description; however, this study deals with the analysis of microscopic images of specially prepared samples.…”