Objectives: Intraoperative tumor visualization with 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence is widely applied for improved resection of high-grade gliomas. However, visible fluorescence is present only in a minority of low-grade gliomas (LGGs) according to current literature. Nowadays, antiepileptic drugs (AEDs) are frequently administered to LGG patients prior to surgery. A recent in-vitro study demonstrated that AEDs result in significant reduction of PpIX synthesis in glioma cells. The aim of this study was thus to investigate the role of 5-ALA fluorescence in LGG surgery and the influence of AEDs on visible fluorescence. Patients and Methods: Patients with resection of a newly diagnosed suspected LGG after 5-ALA (25 mg/kg) administration were initially included. During surgery, the presence of visible fluorescence (none, mild, moderate, or bright) within the tumor and intratumoral fluorescence homogeneity (diffuse or focal) were analyzed. Tissue samples from fluorescing and/or non-fluorescing areas within the tumor and/or the assumed tumor border were collected for histopathological analysis (WHO tumor diagnosis, cell density, and proliferation rate). Only patients with diagnosis of LGG after surgery remained in the final study cohort. In each patient, the potential preoperative intake of AEDs was investigated. Results: Altogether, 27 patients with a histopathologically confirmed LGG (14 diffuse astrocytomas, 6 oligodendrogliomas, 4 pilocytic astrocytomas, 2 gemistocytic astrocytomas, and one desmoplastic infantile ganglioglioma) were finally included. Visible fluorescence was detected in 14 (52%) of 27. In terms of fluorescence homogeneity (n = 14), 7 tumors showed diffuse fluorescence, while in 7 gliomas focal fluorescence was noted. Cell density (p = 0.03) and proliferation rate (p = 0.04) was significantly higher in fluorescence-positive than in fluorescence-negative samples. Furthermore, 15 (56%) of 27 patients were taking AEDs before surgery. Of these, 11 patients (73%) showed no visible fluorescence. In contrast, 10 (83%) of 12 patients without prior AEDs intake showed visible fluorescence. Thus, visible fluorescence was significantly more common in patients without AEDs compared to patients with preoperative AED intake (OR = 0,15 (CI 95% 0.012–1.07), p = 0.046). Conclusions: Our study shows a markedly higher rate of visible fluorescence in a series of LGGs compared to current literature. According to our preliminary data, preoperative intake of AEDs seems to reduce the presence of visible fluorescence in such tumors and should thus be taken into account in the clinical setting.

In the present study, various combinations of dimensionality reduction methods with data clustering methods for the analysis of biopsy samples of intracranial tumors were investigated. Fresh biopsies of intracranial tumors were studied in the Laboratory of Neurosurgical Anatomy and Preservation of Biological Materials of N.N. Burdenko Neurosurgery Medical Center no later than 4 h after surgery. The spectra of Protoporphyrin IX (Pp IX) fluorescence, diffuse reflectance (DR) and Raman scattering (RS) of biopsy samples were recorded. Diffuse reflectance studies were carried out using a white light source in the visible region. Raman scattering spectra were obtained using a 785 nm laser. Patients diagnosed with meningioma, glioblastoma, oligodendroglioma, and astrocytoma were studied. We used the cluster analysis method to detect natural clusters in the data sample presented in the feature space formed based on the spectrum analysis. For data analysis, four clustering algorithms with eight dimensionality reduction algorithms were considered.

Decision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These systems provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectively, these elements represent artificial intelligence (AI) in neuro-oncology. This paper reviews recent studies which apply machine learning algorithms to optical spectroscopy data from central nervous system (CNS) tumors, both ex vivo and in vivo. We first cover general issues such as the physical basis of the optical-spectral methods used in neuro-oncology, and the basic algorithms used in spectral signal preprocessing, feature extraction, data clustering, and supervised classification methods. Then, we review in more detail the methodology and results of applying ML techniques to fluorescence, elastic and inelastic scattering, and IR spectroscopy.

Appropriate analysis of biological tissue deep regions is important for tumor targeting. This paper is concentrated on photons’ paths analysis in such biotissue as brain, because optical probing depth of fluorescent and excitation radiation differs. A method for photon track reconstruction was developed. Images were captured focusing on the transparent wall close and parallel to the source fibres, placed in brain tissue phantoms. The images were processed to reconstruct the photons most probable paths between two fibres. Results were compared with Monte Carlo simulations and diffusion approximation of the radiative transfer equation. It was shown that the excitation radiation optical probing depth is twice more than for the fluorescent photons. The way of fluorescent radiation spreading was discussed. Because of fluorescent and excitation radiation spreads in different ways, and the effective anisotropy factor,geff, was proposed for fluorescent radiation. For the brain tissue phantoms it were found to be0.62±0.05and0.66±0.05for the irradiation wavelengths 532 nm and 632.8 nm, respectively. These calculations give more accurate information about the tumor location in biotissue. Reconstruction of photon paths allows fluorescent and excitation probing depths determination. Thegeffcan be used as simplified parameter for calculations of fluorescence probing depth.

During intraoperative fluorescence navigation to remove various neoplasms and during pharmacokinetic studies of photosensitizers in laboratory animals, in many cases, the ratio of photosensitizer accumulation in the tumor and normal tissue can reach ⩾ 10-fold, which inevitably changes their optical properties. At the same time, the tumor formation process causes various metabolic and structural changes at cellular and tissue levels, which lead to changes in optical properties. A hardware-software complex for the spectral-fluorescence studies of the content of fluorochromes in biological tissues with significantly different optical properties was developed, and it was tested on optical phantoms with various concentrations of photosensitizers, absorbers, and scatterers. To correct the influence of optical properties on the photosensitizer concentration analysis by fluorescence spectroscopy, we propose the spectrum-processing algorithm, which combines empirical and theory-based approaches.