## 5 Conclusions  
In this work, the popular glioblastoma model based on glioblastoma cells' injection into the mouse brain was used. But the injection itself can be considered as a TBI, and it is necessary to differentiate metabolic disorders in the mouse brain caused by traumatic brain injuries or/and glioblastoma development. Mouse blood serum was analyzed by THz spectroscopy. In the first step, the homogeneity of the experimental groups with a cultural medium injection was tested by unsupervised ML methods to prove that CMI group 1 (day 7 after CMI) was associated with a TBI state. In the second stage, this group was compared with the group with the largest volume of glioblastoma (day 21 after injection of glioblastoma cells) to differentiate between glioblastoma and TBI. The constructed prediction data models were verified by the 10-fold cross-validation technique coupled with an ROC-AUC analysis to ensure the repeatability of the results. Finally, informative THz bands were selected, which had the most impact on the separability between the glioblastoma group and the TBI group. As far as we know, this is the first work in which differences in the blood serum THz spectra from small animals with glioblastoma and TBI were studied. The presented results can be further expanded to the study of patient blood and appropriate diagnostic method development. Data quality strongly affects the prediction data model efficiency. The robustness of these models can be increased by optimizing the hyperparameters. Considering a combination of such parameters for each method in the ML pipeline leads to high computational costs. Understanding what effect each parameter has becomes crucial. The limitations of the presented work are the following. ML models require plenty of marked data to construct a reliable prediction data model, but glioma models are subjected to a high variance due to the complex physiology of the carcinogenic process in glia. Only \"white box\" algorithms were tested, yet complex \"black box\" models coupled with the SHAP technique may produce better results. Still, the latter is more computationally intensive. THz spectroscopy of biofluids is difficult because of strong water absorption, so preliminary drying/lyophilization of samples is preferable. The binary classifier performance metrics can also be enriched by the use of Matthews correlation coefficient, which is argued to be the next gold standard in the evaluation of supervised ML models. Future work will be related to the data fusion of different modalities to increase diagnostic potential and reveal the metabolites of glioma in the blood. This metabolic profile can be discovered by Raman spectroscopy or MRS. Another option for improvements in ML models is a combination of THz and MRI/PET/CT imaging to visualize tumor boundaries that are crucial for surgery [81] and differentiate glioblastoma and TBI. Both intraoperative MRI and PET are extremely expensive and time-consuming. PET has a low resolution compared to THz and MRI [82]. The application of computer vision techniques based on convolutional neural networks significantly boosts the segmentation quality of MRI brain scans [83; 84; 85]. A combination of diagnostic MRI enhanced by ML methods and intraoperative THz visualizations could be a promising solution for glioma surgery. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14072872/s1](https://www.mdpi.com/article/10.3390/app14072872/s1)]([https://www.mdpi.com/article/10.3390/app14072872/s1](https://www.mdpi.com/article/10.3390/app14072872/s1)), Experiment; Figure S1: Additional PCA projections of the CMI groups.