Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.

Brain tumor needle biopsies are performed to retrieve tissue samples for neuropathological analysis. Although preoperative images guide the procedure, there are risks of hemorrhage and sampling of non-tumor tissue. This study aimed to develop and evaluate a method for frameless one-insertion needle biopsies with in situ optical guidance and present a processing pipeline for combined postoperative analysis of optical, MRI, and neuropathological data. An optical system for quantified feedback on tissue microcirculation, gray–whiteness, and the presence of a tumor (protoporphyrin IX (PpIX) accumulation) with a one-insertion optical probe was integrated into a needle biopsy kit that was used for frameless neuronavigation. In Python, a pipeline for signal processing, image registration, and coordinate transformation was set up. The Euclidian distances between the pre- and postoperative coordinates were calculated. The proposed workflow was evaluated on static references, a phantom, and three patients with suspected high-grade gliomas. In total, six biopsy samples that overlapped with the region of the highest PpIX peak without increased microcirculation were taken. The samples were confirmed as being tumorous and postoperative imaging was used to define the biopsy locations. A 2.5 ± 1.2 mm difference between the pre- and postoperative coordinates was found. Optical guidance in frameless brain tumor biopsies could offer benefits such as quantified in situ indication of high-grade tumor tissue and indications of increased blood flow along the needle trajectory before the tissue is removed. Additionally, postoperative visualization enables the combined analysis of MRI, optical, and neuropathological data.

High-grade glioma characteristics such as heterogeneity and diffuse growth present a major diagnostic and therapeutic challenge, making accurate imaging essential for diagnosis and surgical planning. Diffusion MRI (dMRI) shows promise in tissue identification through a negative correlation between the dMRI apparent diffusion coefficient and tumor cellularity. Further, tissue disorganization due to tumor growth is correlated with decreased fractional anisotropy (FA) from diffusion tensor imaging (DTI). Q-space trajectory imaging (QTI) through free gradient waveform encoding during dMRI acquisition has been suggested as a framework for dMRI scalar map generation, enabling disentangled measures of shape, size, and orientation. We aimed to extend a clinically integrated workflow for optical guidance in frameless navigated brain tumor biopsies to include DTI and QTI scalars for multimodal analysis. Diffusion scalars were compared to tumor indications on tissue fluorescence, conventional imaging, and neuropathology in navigated brain tumor biopsy procedures. In seven high-grade glioma patients, the biopsied tissue volume was associated with decreased dMRI features (anisotropy, kurtosis, and order parameters) and increased diffusivity in DTI when compared with contralateral white matter. Principal components of diffusion scalars depend on microstructural (QTI) and diffusivity (DTI) parameters, respectively. Redundancy analysis between the dMRI scalars revealed scalar pairs that offer novel information for tissue separation that could be of interest for fine-tuning of the MRI protocol before further evaluation of QTI for tumor tissue identification in the clinical setting.

Background: Brain tumor needle biopsy interventions are inflicted with nondiagnostic or biased sampling in up to 25% and hemorrhage, including asymptomatic cases, in up to 60%. To identify diagnostic tissue and sites with increased microcirculation, intraoperative optical techniques have been suggested. The aim of this study was to investigate the clinical implications of in situ optical guidance in frameless navigated tumor biopsies. Methods: Real-time feedback on protoporphyrin IX (PpIX) fluorescence, microcirculation, and gray-whiteness was given before tissue sampling (272 positions) in 20 patients along 21 trajectories in total. The primary variables of investigation were fluorescence in relation to neuropathological findings and gadolinium (Gd) enhancement, increased cerebral microcirculation in relation to bleeding incidence, number of trajectories, and impact on operation time. Results: PpIX fluorescence was detected in Glioblastoma IDH-wildtype CNS WHO grade 4 (n = 12), Primary diffuse large B-cell lymphoma (n = 3), astrocytoma IDH-mutated CNS WHO grade 4 (n = 1) (Ki67 indices ≥ 15%). For 2 patients, no PpIX fluorescence or Gd was found, although samples contained tumorous tissue (Ki67 index 6%). Increased microcirculation was found along 9 trajectories (34 sites), located in cortical, tumorous, or tentorium regions. Postoperative bleedings (n = 10, nine asymptomatic) were related to skull opening or tissue sampling. This study strengthens the proposed independence from intraoperative neuropathology as PpIX fluorescence is detected. Objective real-time feedback resulted in fewer trajectories compared to previous studies indicating reduced operation time. Conclusions: The integrated optical guidance system provides real-time feedback in situ, increasing certainty and precision of diagnostic tissue before sampling during frameless brain tumor biopsies.

Background/Objectives: Primary brain tumors are difficult to identify intraoperatively due to their infiltrative character in the marginal zone. Several optical methods have been suggested. Of these, 5-ALA-induced fluorescence visualized through a microscope is the most common. The aim is to present an investigational probe-based optical system and its translation for clinical use, summarize previous studies, and give examples of clinical implementations during resection and burr hole biopsies. Methods: The FluoRa system combines 5-ALA fluorescence spectroscopy with laser Doppler flowmetry (LDF). Probe designs are available for brain tumor resection (hand-held probe) or burr hole needle biopsies (frame-based or navigated). The outer cannulas of biopsy needles are modified with an opening at the tip for simultaneous use with optical probes during insertion along the trajectory. An updated version of FluoRa is introduced and experimentally investigated. Results: Probe-based fluorescence spectroscopy has been successfully translated for clinical use and applied during brain tumor resection (n = 75) and burr hole needle biopsies (n = 47). Forward-looking optical measurements through the biopsy needle reduce the number of trajectories (28/27) compared to prior to insertion (28/20), at the same time that the target for tissue sampling can be identified in situ. Additionally, increased microcirculation is identified along the trajectory with LDF. This is accomplished with FluoRa. Conclusions: Intraoperative probe-based spectroscopic measurements quantify 5-ALA fluorescence and thus identify glioblastoma and lymphoma tissue in situ during resection and burr hole needle biopsies.

Background: Quantitative MRI (qMRI) relaxometry holds potential for brain tumor identification beyond contrast enhancement on conventional images. However, clinical implementation is limited by long acquisition times, changing conditions between imaging and surgery, and lack of correlation with standard techniques. Purpose: To extend a methodology for multimodal data analysis to relaxometry data. To integrate relaxometry into the burr hole needle biopsy procedure with optical guidance, setup a workflow for multimodal data processing and analysis, and apply the methodology in a clinical setting. Methods: Multi-dimensional multi-echo relaxometry data (2x6 min) was acquired in addition to the clinical imaging protocol. Relaxation rate and proton density maps, as well as their differences were calculated before (R₁, R₂) and after gadolinium contrast-agent administration (R₁Gd, R₂Gd). Radiological volumes of interest (VOIs: tumor, edema, white matter, and biopsy) were defined on clinical images. Rate distribution changes were analyzed on three levels: the biopsied volume, along the needle trajectory (4x4x4 mm³ volumes), and VOIs. Increased R₁Gd and R₂Gd were compared to indications from 5-aminolevulinic acid-induced fluorescence and detailed neuropathological evaluation. Results: Neuropathological analysis confirmed seven glioblastoma, one lymphoma, and one non-tumorous diagnosis. Increased R₁Gd was found in all biopsied volumes, although tumorous volumes presented larger R₁Gd increase (3–9 times) compared to volumes dominated by necrotic or non-tumorous tissue. Along the trajectory, increased R₁Gd and R₂Gd were not tumor-specific, however, the greatest R₁Gd shifts were found in or adjacent to radiologically defined tumorous tissue. Increased relaxation rates corresponded to 82% and 45% (R₁Gd: φ = 0.35, R₂Gd: φ = 0.27) of fluorescence peaks. In the radiological VOIs, increased R₁Gd and R₂Gd were found in tumorous tissue, a slight right shift in edematous tissue, and negligible changes in white matter. Conclusion: Combined analysis suggests increased R₁Gd together with fluorescence peaks as a marker for tumor tissue. The presented multimodal approach provides a workflow toward clinical translation of relaxometry.