# Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
## 4 Discussion  
The blood of cancer patients contains tumor molecular biomarkers [21; 22; 23], but their identification is not a trivial task [61]. THz spectroscopy is sensitive to the presence of biomolecules in a sample [30]. For example, a correlation of THz absorption with concentrations of serum glucose [36; 38] and protein [30; 40] has been demonstrated. However, molecules' absorption bands overlapping in the THz spectral range make it difficult to discover specific metabolites in blood samples (both serum and plasma) by conventional statistical analysis due to correlations between frequencies and the high dimensionality of the data [62]. Machine learning methods allow these problems to be overcome, build predictive data models, and discover informative sub-bands in THz spectral range [36; 38; 39; 41; 63].  
In this work, we applied PCA and t-SNE dimensionality reduction methods to the THz spectral data of human blood plasma. According to the analysis conducted using t-SNE, the measured THz spectra from a sample are close to each other in the feature space, which indicates the acceptable homogeneity of the THz data for an individual blood plasma sample. At the stage of predictive data model construction, both XGBoost and RF methods were shown to work more accurately than SVM: they provided a better separation of the glioma patients' group from healthy controls and patients with SCD.  
Our methods can be compared to the ones proposed in [42]. In this paper, rat blood serum at different degrees of experimental blast-induced traumatic brain injury was studied by THz-TDS and ML methods including PCA and k-Nearest neighbor (kNN) and SVM algorithms. In our previous study, we also studied small animal models and the classification accuracy of the SVM classifier measured by ROC-AUC analysis was good [41]. Instead of using the \(p\)-value, which has recently been criticized, we prefer ML methods such as Isolation Forest, PCA, and t-SNE for visual analysis. Another option that is missed is data standardization, usually recommended prior to PCA. We studied this topic, and it has demonstrated that THz data standardization can decrease the performance of the classifier. Performance metrics such as accuracy, sensitivity, and specificity are usually presented as mean \(\pm\) standard deviation (STD), because training models on small dataset can lead to drastic variance due to outliers in groups. Therefore, we prefer mean ROC-AUC with an STD interval.  
The major challenge is the non-invasive control of the effectiveness of the tumor removal surgery [64]. According to the presented results, RF and XGBoost predictive data models made it possible to distinguish the blood plasma of glioma patients before and after surgery with good discrimination: AUC was 0.92 and 0.86, respectively. The SVM did not show a satisfactory result (AUC = 0.5). The most informative frequencies were 0.56 and 1.2 THz. These frequencies coincided with informative frequencies obtained in an experimental mouse model of human glioblastoma [41]. However, an unresolved problem was finding correspondence between metabolites in blood plasma and informative THz frequencies. A positive moment was that we discovered an informative THz range for glioma diagnostics, and it was valuable information for making cheaper devices.