# Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
## 1 Introduction
The molecular composition of body fluids changes significantly with the tumor appearance and development [23; 24; 25], which can be a good diagnostic criterion for early cancer diagnosis. Body fluid glioma molecular markers include circulating tumor DNA and RNA, proteins, and metabolites [22; 25]. Most of these markers have specific dielectric characteristics in the terahertz (THz) range, which can potentially be used for the early diagnosis of gliomas [23; 26]. THz technologies have already shown their applicability in noninvasive diagnostics of various pathologies [27; 28; 29]. Many molecular markers, including water [30], have spectral imprints in the THz range, providing a differentiation of healthy tissues from neoplas [31; 32; 33; 34; 35]. The combination of terahertz time-domain spectroscopy (THz-TDS) and machine learning (ML) has made it possible to perform differential diagnostics of thyroid nodule malignancy by analyzing patients' blood plasma [36]. A spectral analysis of exhaled air and blood plasma by THz spectroscopy showed the promise of this approach for diabetes diagnosis [37; 38]. The analysis of saliva from patients with oral lichen planus (OLP) using THz-TDS also showed a good separation between the control group and those with OLP [39]. The difference in the THz spectra of rats' blood serum in the dynamics of experimental liver cancer and its correlation with changing the blood biochemical composition have been established [40]. The possibility of detecting glioma stages through mouse blood serum analysis using THz-TDS has been demonstrated [41]. THz absorption spectra do not have sharp peaks [30; 36; 38; 40] and are difficult to interpret using mathematical statistics methods. ML is a powerful technique for THz data characterization, including the differentiation of datasets with slightly differing THz spectra. The typical machine learning pipeline (MLP) includes steps aimed at noise reduction, outlier removal, informative features selection/extraction, prediction data model creation, etc. [41; 42]. Outlier removal is usually performed with the isolation forest method [43]. The drawback of the latter is associated with threshold value choice, which is not trivial. Another method for outlier detection is a visual analysis by t-distribution stochastic neighborhood embedding (t-SNE) [44]. The advantage of the latter is the possibility of checking similarity in the original data before their dimension reduction [45], contrary to principal component analysis (PCA). Supervised ML methods allow the selection of the most informative features in high dimensional spectral data [41; 46]. The choice of supervised ML methods is conditioned by the requirements of model interpretability. The top-scored performance algorithms are Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANNs) [47]. ANN lacks the explainability of constructed data models. To achieve it, a distillation approach [48] or external methods such as SHapley Additive exPlanations (SHAP) are used [49]. In turn, SHAP is limited by a huge computational time for high-dimensional data [50]. Sequential feature selection methods have poor performance on a high dimensional and inter-correlated data [51]. On the other hand, SVM and tree-based algorithms provide reliable embedded feature selection [52; 53; 54]. In this work, we performed a comparative study of the blood plasma of glioma patients and patients with skull craniectomy defects (SCD), as well as healthy donors, using THz-TDS and ML. We investigated the applicability of the ML model initially designed for the separation of the blood plasma THz spectra of experimental U87 glioblastoma and healthy mouse groups [41] to human patients with glioma, and compared the performances of RF, XGBoost, and SVM for THz spectra analysis. The blood plasma samples of glioma patients before and after tumor removal surgery were also examined.