[PET/CT with 11C-methionine in assessment of brain glioma metabolism]. / Pozitronnaya emissionnaya tomografiya v sochetanii s komp'yuternoi tomografiei i 11S-metioninom v otsenke metabolizma gliom golovnogo mozga.

OBJECTIVE: To study 11C-methionine (MET) metabolism in gliomas using CNS tumor biobank imaging data. MATERIAL AND METHODS: MRI and 11C-MET PET/CT were performed in 225 patients (49±14 years, M/F=84/101) according to standard protocols with analysis of 11C-MET accumulation index and volumetric parameters (V_FLAIR, V_PET and V_PET/FLAIR). These results were compared with molecular genetic testing and 2-year overall survival. RESULTS: We examined 225 patients with gliomas (97 glioblastomas, 70 astrocytomas, 58 oligodendrogliomas). Accumulation index and volume of 11C-MET in glioblastomas were significantly higher in the general group (AI=2.90, Se 69%, Sp 76%, AUC 0.76; V_PET=24.3 cm3, Se 67%, Sp 60%, AUC 0.65; V_PET/FLAIR 0.46, Se 60%, Sp 69%, AUC 0.67) and within the group of astrocytomas (AI=2.93, Se 68%, Sp 89%, AUC 0.84; V_PET=8.06 cm3, Se 91%, Sp 35%, AUC 0.66; V_PET/FLAIR 0.27, Se 77%, Sp 60%, AUC 0.71). The median 2-year overall survival in patients with glioblastomas was 13 months that was significantly lower compared to IDH «+¼ gliomas (p<0.0001). There was a relationship between high accumulation index of 11C-MET and shorter overall survival in patients with glioblastomas. Significantly higher AI >3.59 (Se 89%, Sp 67%, AUC 0.79) was additionally obtained in subgroup of patients with glioblastomas >50 years (n=34) for EGFR «+¼ tumors. CONCLUSION: We found variable 11C-MET metabolism in WHO 2021 gliomas and confirmed significant difference in metabolic activity and volume of 11C-MET accumulation in glioblastomas compared to IDH «+¼ gliomas. Moreover, we revealed the relationship between high accumulation index and shorter survival. Analysis of 11C-MET metabolism in patients over 50 years old revealed higher accumulation index in the EGFR «+¼ group. Further comparison of these imaging methods and assessment of other significant mutations are necessary to identify the anatomical and metabolic patterns of IDH «+¼ gliomas.

[Quantitative analysis of orbit volume and eyeball position after resection of hyperostotic sphenoid wing meningioma and simultaneous orbital wall reconstruction using individual technologies]. / Kolichestvennyi analiz ob"emov glaznits i pozitsii glaznykh yablok posle udaleniya giperostoticheskikh meningiom kryl'ev osnovnoi kosti i odnomomentnoi rekonstruktsii stenok glaznitsy s ispol'zovaniem individual'nykh tekhnologii.

OBJECTIVE: To conduct a quantitative analysis of orbit volume at different stages of preparation and surgical treatment of patients with cranio-orbital meningiomas undergoing resection with simultaneous orbital wall reconstruction using 3D modeling and 3D printing technologies. MATERIAL AND METHODS: A prospective cohort non-randomized study included 24 patients with cranio-orbital meningiomas. The volumes were measured by segmenting the orbital structures using the planimetric method in the Inobitec PRO software package. Three expert neurosurgeons independently performed these measurements. The implants were modeled in Blender software. We used the intraclass correlation coefficient (ICC) and global similarity index (GSI) to analyze interrater agreement and ensure reproducibility of analysis. RESULTS: Interrater agreement on orbital markings was very high for both metrics (ICC and GSI). The ICC (A,3) for intact orbit volume was 0.99 (95% CI 0.981; 0.996, p=1.9962018^{-33}), 0.99 (95% CI 0.983; 0.996, p=1.903203^{-34}) for damaged orbit volume at the preoperative stage, 0.99 (95% CI 0.979; 0.995, p=3.5939828^{-32}) for damaged orbit volume at the stage of modeling of resection and reconstruction, 0.99 (95% CI 0.978; 0.995, p=1.1048941^{-30}) for damaged orbit volume in postoperative period. The ICC for measurements related to EI was 0.94-0.97 (very high). This analysis revealed a strong inverse relationship between EI and volume index at the preoperative stage (rho= -0.55, p=0.004987), as well as between dynamics of EI and volume indexes in perioperative period (rho= -0.59, p=0.003). We found significant relationship (p=0.006757) between implant displacement in the area of lateral orbital wall and differences of actual and theoretical volumes. CONCLUSION: The proposed method of planimetric contouring and segmentation of orbital volumes is highly accurate and reproducible. Significant patterns allow us to develop predictive models for preliminary calculation of target volume of the damaged orbit.

[Prognostic model for assessing the human glioma cell malignancy grade based on MDM2, MELK, SOX2, CDK4, DR5 and OCT4 gene expression]. / Prognosticheskaya model' dlya otsenki stepeni zlokachestvennosti kul'tury kletok gliomy cheloveka na osnovanii issledovaniya ekspressii paneli genov MDM2, MELK, SOX2, CDK4, DR5 i OCT4.

Glioma cell cultures are used in basic researches of tumor processes, personalized medicine for selecting treatment regimens depending on individual characteristics of patients and pharmacology for assessing the effectiveness of chemotherapy. Suppression of glioma culture growth without reduction of malignancy grade is common. Drug cancellation may be followed by substitution of precursor cells by more malignant clones. Therefore, analysis of culture cell malignancy grade is important. In the future, intraoperative analysis of glioma cell malignancy grade can be used to select individual therapy. OBJECTIVE: We analyzed the relationship between expression of marker genes TUBB3, CD133, CDK4, CDK6, CIRBP, DR4, DR5, EGFR, FGFR, FSHR, GDNF, GFAP, L1CAM, LEF1, MAP2, MDM2, MELK, NANOG, NOTCH2, OCT4, OLIG2, PDGFRA, PDGFA, PDGFB and SOX2 and glioma cell malignancy grade, as well as created appropriate prognostic model. MATERIAL AND METHODS: We analyzed expression of 25 marker genes in 22 samples of human glioma cultures using quantitative real-time PCR. Statistical analysis was performed using the IBM SPSS Statistics 26.0 software. We used the Kolmogorov-Smirnov and Shapiro-Wilk tests to assess distribution normality. Nonparametric Jonckheere-Terpstra and Spearman tests were applied. RESULTS: We obtained a prognostic model for assessing the grade III and IV glioma cell malignancy based on expression of marker genes MDM2, MELK, SOX2, CDK4, DR5 and OCT4. Predictive accuracy was 83% (Akaike information criterion -55.125).

[Non-invasive diagnosis of brain gliomas by histological type using neuroradiomics in standardized regions of interest: towards digital biopsy]. / Neinvazivnaya diagnostika gliom golovnogo mozga po gistologicheskomu tipu s pomoshch'yu neiroradiomiki v standartizirovannykh zonakh interesa: na puti k tsifrovoi biopsii.

The future of contemporary neuroimaging does not solely lie in novel image-capturing technologies, but also in better methods for extraction of useful information from these images. Scientists see great promise in radiomics, i.e. the methodology for analysis of multiple features in medical image. However, there are certain issues in this field impairing reproducibility of results. One such issue is no standards in establishing the regions of interest. OBJECTIVE: To introduce a standardized method for identification of regions of interest when analyzing MR images using radiomics; to test the hypothesis that this approach is effective for distinguishing different histological types of gliomas. MATERIAL AND METHODS: We analyzed preoperative MR data in 83 adults with various gliomas (WHO classification, 2016), i.e. oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma, and glioblastoma. Radiomic features were computed for T1, T1-enhanced, T2 and T2-FLAIR modalities in four standardized volumetric regions of interest by 356 voxels (46.93 mm3): 1) contrast enhancement; 2) edema-infiltration; 3) area adjacent to edema-infiltration; 4) reference area in contralateral hemisphere. Subsequently, mathematical models were trained to classify MR-images of glioma depending on histological type and quantitative features. RESULTS: Mean accuracy of differential diagnosis of 4 histological types of gliomas in experiments with machine learning was 81.6%, mean accuracy of identification of tumor types - from 94.1% to 99.5%. The best results were obtained using support vector machines and random forest model. CONCLUSION: In a pilot study, the proposed standardization of regions of interest demonstrated high effectiveness for MR-based differential diagnosis of oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma and glioblastoma. There are grounds for applying and improving this methodology in further studies.

Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based on 11C-Methionine PET/CT in Glioblastoma.

Modern methodology of PET/CT quantitative analysis in patients with glioblastomas is not strictly standardized in clinic settings and does not exclude the influence of the human factor. Methods of radiomics may facilitate unification, and improve objectivity and efficiency of the medical image analysis. The aim of the study is to evaluate the potential of radiomics in the analysis of PET/CT glioblastoma images identifying the relationship between the radiomic features and the 11С-methionine tumor-to-normal brain uptake ratio (TNR) determined by an expert in routine. Materials and Methods: PET/CT data (2018-2020) from 40 patients (average age was 55±12 years; 77.5% were males) with a histologically confirmed diagnosis of "glioblastoma" were included in the analysis. TNR was calculated as a ratio of the standardized uptake value of 11C-methionine measured in the tumor and intact tissue. Calculation of radiomic features for each PET was performed in the specified volumetric region of interest, capturing the tumor with the surrounding tissues. The relationship between TNR and the radiomic features was determined using the linear regression model. Predictors were included in the model following correlation analysis and LASSO regularization. The experiment with machine learning was repeated 300 times, splitting the training (70%) and test (30%) subsets randomly. The model quality metrics and predictor significance obtained in 300 tests were summarized. Results: Of 412 PET/CT radiomic parameters significantly correlated with TNR (p<0.05), the regularization procedure left no more than 30 in each model (the median number of predictors was 9 [7; 13]). The experiment has demonstrated a non-random linear correlation (the Spearman correlation coefficient was 0.58 [0.43; 0.74]) between TNR and separate radiomic features, primarily fractal dimensions, characterizing the geometrical properties of the image. Conclusion: Radiomics enabled an objective determination of PET/CT image texture features reflecting the biological activity of glioblastomas. Despite the existing limitations in the application, the first results provide a good perspective of these methods in neurooncology.

Artificial Intelligence Technologies in the Microsurgical Operating Room (Review).

Surgery performed by a novice neurosurgeon under constant supervision of a senior surgeon with the experience of thousands of operations, able to handle any intraoperative complications and predict them in advance, and never getting tired, is currently an elusive dream, but can become a reality with the development of artificial intelligence methods. This paper has presented a review of the literature on the use of artificial intelligence technologies in the microsurgical operating room. Searching for sources was carried out in the PubMed text database of medical and biological publications. The key words used were "surgical procedures", "dexterity", "microsurgery" AND "artificial intelligence" OR "machine learning" OR "neural networks". Articles in English and Russian were considered with no limitation to publication date. The main directions of research on the use of artificial intelligence technologies in the microsurgical operating room have been highlighted. Despite the fact that in recent years machine learning has been increasingly introduced into the medical field, a small number of studies related to the problem of interest have been published, and their results have not proved to be of practical use yet. However, the social significance of this direction is an important argument for its development.

[Pulmonary embolism in neurosurgical patients]. / Tromboemboliya legochnoi arterii u patsientov neirokhirurgicheskogo profilya.

Pulmonary embolism (PE) is a serious problem for neurosurgical patients because of high risk of mortality and the need to choose effective and safe anticoagulation. OBJECTIVE: To analyze the patients with PE after neurosurgical interventions. MATERIAL AND METHODS: A prospective study was performed at the Burdenko Neurosurgical Center between January 2021 and December 2022. Inclusion criteria were neurosurgical disease and PE. RESULTS: In accordance with inclusion criteria, we analyzed 14 patients. Mean age was 63 [45.8; 70.0] years. Four patients died. PE was a direct cause of death in 1 case. PE occurred in 5.14±3.68 days after surgery. Anticoagulation was safely implemented in 3 patients with PE on the first day after craniotomy. In a patient with massive PE several hours after craniotomy, anticoagulation resulted hematoma with brain dislocation and death. Thromboextraction and thrombodestruction were used in 2 patients with massive PE and high risk of mortality. CONCLUSION: Despite low incidence (0.1%), PE is a serious problem in neurosurgical patients due to the risk of intracranial hematoma under effective anticoagulant therapy. In our opinion, endovascular interventions with thromboextraction, thrombodestruction or local fibrinolysis are the safest in the treatment of PE after neurosurgery. Individual approach considering clinical, laboratory data, advantages and disadvantages of a particular anticoagulant drug is required when choosing the tactics of anticoagulation. Further analysis of a larger number of clinical cases is needed to develop the guidelines for the management of neurosurgical patients with PE.

[Surgical and combined treatment of patients with craniovertebral junction meningioma: a single-center retrospective study of 196 cases]. / Analiz rezul'tatov khirurgicheskogo i kombinirovannogo lecheniya patsientov s meningiomami oblasti kraniovertebral'nogo perekhoda: odnotsentrovoe retrospektivnoe issledovanie 196 sluchaev.

Treatment of craniovertebral junction meningioma is a difficult task. Surgical treatment is the gold standard for these patients. However, it is associated with high risk of neurological impairment, while combined treatment (surgery + radiotherapy) provides more favorable outcomes. OBJECTIVE: To present the results of surgical and combined treatment of patients with craniovertebral junction meningioma. MATERIAL AND METHODS: There were 196 patients with craniovertebral junction meningioma who underwent surgical or combined (surgery + radiotherapy) treatment at the Burdenko Neurosurgery Center between January 2005 and June 2022. The sample included 151 women and 45 men (3.4:1). Resection of tumor was performed in 97.4% of patients, craniovertebral junction decompression with dural defect closure - 2%, ventriculoperitoneostomy - 0.5%. As the second stage, 40 patients (20.4%) underwent radiotherapy. RESULTS: Total resection was achieved in 106 patients (55.2%), subtotal - 63 (32.8%), partial - 20 (10.4%), tumor biopsy was performed in 3 (1.6%) cases. Intraoperative complications occurred in 8 patients (4%), postoperative complications - in 19 (9.7%) cases. Radiosurgery was carried out in 6 (15%) patients, hypofractionated irradiation - 15 (37.5%), standard fractionation - 19 (47.5%) patients. Tumor growth control after combined treatment made up 84%. CONCLUSION: Clinical outcomes in patients with craniovertebral junction meningioma depend on tumor dimensions, topographic and anatomical localization of tumor, resection quality and relationship with surrounding structures. Combined treatment of anterior and anterolateral meningiomas of the craniovertebral junction is preferable compared to total resection.

[Infectious complications in acute period after traumatic brain injury]. / Infektsionnye oslozhneniya u patsientov v ostrom periode cherepno-mozgovoi travmy.

Patients with traumatic brain injury (TBI) are at high risk of infection. OBJECTIVE: To delineate infections in acute period of TBI, association between intracranial lesion type and risk of infection, as well as to estimate treatment outcomes in these patients depending on infection. MATERIAL AND METHODS: This study included 104 patients with TBI (80 men and 24 women) aged 33.01±14.35 years. All patients met the inclusion criteria: admission within 72 hours after TBI, age 18-75 years, ICU-stay >48 hours, available brain MRI data. Mild, moderate and severe TBI were diagnosed in 7%, 11% and 82% of patients, respectively. Analysis of infections was performed in accordance with the definitions of the Centers for Disease Control/National Healthcare Safety Network (CDC/NHSN). RESULTS: Acute period of TBI is associated with high incidence of infection (73%), and prevalent infection is pneumonia (58.7%). Severe intracranial damage in acute period of TBI (grade 4-8 according to MR-based classification by A.A. Potapov and N.E. Zakharova) is associated with higher incidence of infection. Infectious complications more than twice increase duration of mechanical ventilation, ICU- and hospital-stay. CONCLUSION: Infectious complications significantly affect treatment outcomes in acute period of TBI increasing duration of mechanical ventilation, ICU- and hospital-stay.

Craniotomy size determines the neurosurgeon - microscope interaction: A proof-of-concept study.

PURPOSE: Our study aimed to compare the differences in quantitative parameters, describing the processes of neurosurgeons' interaction with a microscope when performing traditional and minimally invasive approaches. METHODS: We designed a prospective observational study to assess the quantitative parameters of neurosurgeon-microscope interactions. Eighteen patients with intracranial tumors were enrolled in the research. All cases were divided into 2 groups: patients with deep-seated tumors, which were operated on with craniotomy apertures of standard sizes - 3 to 4.5 cm (standard craniotomy SC group) and patients with deep-seated tumors operated on with minimally invasive burr hole approaches with a diameter of 1.4 cm (BH group). Three video cameras were used to register and analyze surgeon-operating microscope interactions. The interaction of the neurosurgeon with the microscope was described by the following parameters: microscope repositioning; time of work at low, medium, and high magnification; and the number of changes in focal length. All the interaction parameters were measured per minute of the microsurgical operation stage. RESULTS: Nine parameters significantly differed (p value < 0.05) between groups: "total time needed for all microscope positioning adjustments", "number of microscope positioning adjustments per minute","average duration of one microscope position adjustment","operating time at high magnification", "operating time at low magnification","proportion of operating time at high magnification", "proportion of operating time at low magnification", "total time for interaction between neurosurgeon and microscope", "proportion of total time for interaction". Significant P values withstood Benjamini-Hochberg's adjustment for all variables. CONCLUSION: The results of the study provide the grounds to postulate that there is a direct and significant relationship between the size of the craniotomy and the frequency of microscope repositioning, as well as the degree of intraoperative microscope magnification.

[MR-guided non-invasive typing of brain gliomas using machine learning]. / Pervye rezul'taty neinvazivnogo tipirovaniya gliom golovnogo mozga po dannym magnitno-rezonansnoi tomografii s pomoshch'yu mashinnogo obucheniya.

Gliomas are the most common neuroepithelial brain tumors. The modern classification of tumors of central nervous system and treatment approaches are based on tissue and molecular features of a particular neoplasm. Today, histological and molecular genetic typing of tumors can only be carried out through invasive procedures. In this regard, non-invasive preoperative diagnosis in neurooncology is appreclated. One of the perspective areas is artificial intelligence applied for neuroimaging to identify significant patterns associated with histological and molecular profiles of tumors and not obvlous for a specialist. OBJECTIVE: To evaluate diagnostic accuracy of deep learning methods for glioma typing according to the 2007 WHO classification based on preoperative magnetic resonance imaging (MRI) data. MATERIAL AND METHODS: The study included MR scans of patients with glial tumors undergoing neurosurgical treatment at the Burdenko National Medical Research Center for Neurosurgery. All patients underwent preoperative contrast-enhanced MRI. 2D and 3D MR scans were used for learning of artificial neural networks with two architectures (Resnest200e and DenseNet, respectively) in classifying tumors into 4 categories (WHO grades I-IV). Learning was provided on 80% of random examinations. Classification quality metrics were evaluated in other 20% of examinations (validation and test samples). RESULTS: Analysis included 707 contrast-enhanced T1 welghted images. 3D classification based on DenseNet model showed the best result in predicting WHO tumor grade (accuracy 83%, AUC 0.95). Other authors reported similar results for other methods. CONCLUSION: The first results of our study confirmed the fundamental possibility of grading axial contrast-enhanced T1 images according to the 2007 WHO classes using deep learning models.

[Artificial intelligence guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records]. / Prognozirovanie dlitel'nosti gospitalizatsii v neirokhirurgicheskoi klinike po tekstovym dannym elektronnoi meditsinskoi dokumentatsii s pomoshch'yu tekhnologii iskusstvennogo intellekta.

BACKGROUND: Rational use of internal resources of hospitals including bed fund turnover is important objective in high-tech medicine. Machine learning technologies can improve neurosurgical care and contribute to patient-oriented approach. OBJECTIVE: To evaluate the quality of AI-guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records in comparison with expectations of patients and physicians. MATERIAL AND METHODS: AI-guided prediction was based on analysis of unstructured text records of the electronic medical history (preoperative examination and surgical protocol). Predictive models were learned on the data of the Burdenko Neurosurgery Center accumulated for the period 2000-2017 (90.688 cases). Model testing was performed on 111 completed neurosurgical cases in a prospective study. We compared the accuracy of prediction models compared to expectations of patients and physicians regarding hospital-stay. RESULTS: The median absolute error of machine prediction in the final test was 2.00 days. This value was comparable with the doctor's prediction error. CONCLUSION: This study demonstrated the possibility of using unstructured textual data to predict the length of hospital-stay in a neurosurgical hospital.

[Artificial intelligence technologies in clinical neurooncology]. / Tekhnologii iskusstvennogo intellekta v klinicheskoi neiroonkologii.

Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.

Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology.

Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection. Materials and Methods: CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study. Results: The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370. Conclusion: This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.

[Risk factors of nosocomial meningitis in neurological intensive care unit. Results of a five-year prospective study]. / Faktory riska razvitiya nozokomial'nykh meningitov u patsientov neirokhirurgicheskogo profilya v otdelenii reanimatsii i intensivnoi terapii. Rezul'taty pyatiletnego prospektivnogo issledovaniya.

Nosocomial meningitis (NM) is a serious complication in neurosurgery. Understanding the risk factors of nosocomial meningitis is important for their prevention. OBJECTIVE: To determine the main risk factors of NM in neurological intensive care unit. MATERIAL AND METHODS: A prospective study included all patients (n=2140) treated at the neurological intensive care unit for more than 48 hours between October 1, 2010 and October 31, 2015. Cases of nosocomial meningitis were registered. We analyzed risk factors in 2 groups of patients (with and without NM). RESULTS AND DISCUSSION: The incidence of NM in neurological intensive care unit was 8.4% (95% CI 6.8-10.0) (n=180). Relative risk of NM under external ventricular drainage was 3.98 (99 cases of NM, p<0.05). Relative risk of NM in patients with CSF leakage (including nasal CSF leakage after transsphenoidal surgery) was 5.2 (49 cases of NM, p<0.05). The incidence of nosocomial meningitis was significantly higher among patients with surgery time ≥8 hours (17.68%, p<0.01). Nosocomial meningitis was also significantly more common after redo surgery (13.07%, p<0.05). Insertion of intracranial pressure sensor was not a significant risk factor of NM. The risk of meningitis was 2.67% if the above-mentioned factors were absent. Extracranial factors are not specific for NM. No significant relationship between certain neurosurgical disease and NM was found. CONCLUSION: We found higher risk of NM in ICU patients with the following factors: external ventricular drainage, CSF leakage, redo surgery and surgery time.

Observational case series: six neurosurgical patients with septic shock demonstrating clinical improvement after a combination of standard care and blood purification.

BACKGROUND: For patients with primary brain injury, septic shock is especially dangerous due to the possibility of secondary cerebral damage. The key factor of sepsis-associated brain injury is inflammatory mediators, pathogen and damage-associated molecular patterns (PAMPs, DAMPs) release. Theoretically, blood purification may be beneficial for patients with primary brain injury due to its possibility for fast removal of inflammatory mediators. CASE PRESENTATION: We report on six post-neurosurgery septic shock patients treated with combined blood purification (CBP), which included CRRT with high adsorption capacity membrane in combination with CytoSorb adsorber. Clinical improvement in the course of CBP was registered in all patients. Three patients had a stable clinical improvement; the other three patients had only a transient improvement due to underlying neurological and cardiac deficits aggravation. We observed septic shock reversal in four patients. The key observations of the case series are a significant decrease in MOF severity (measured by SOFA score) and in catecholamine need (not statistically significant). By the end of CBP we observed a significant decrease in blood lactate, PCT and IL-6 levels. Two patients demonstrated level of consciousness increase in the setting of CBP therapy measured by GCS and FOUR score. CONCLUSION: This case series demonstrates that CBP therapy may have a role for septic shock patients with primary brain injury.

Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas.

In recent years, the number of scientific publications on artificial intelligence (AI), primarily on machine learning, with respect to neurosurgery, has increased. The aim of the study was to conduct a systematic literature review and identify the main areas of AI applications in neurosurgery. METHODS: Using the PubMed search engine, we found and analyzed 327 original articles published in 1996-2019. The key words specific to each topic were identified using topic modeling algorithms LDA and ARTM, which are part of the AI-based natural language processing. RESULTS: Five main areas of neurosurgery, in which research into AI methods are underway, have been identified: neuro-oncology, functional neurosurgery, vascular neurosurgery, spinal neurosurgery, and surgery of traumatic brain injury. Specifics of these studies are characterized. CONCLUSION: The information presented in this review can be instrumental in planning new research projects in neurosurgery.

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.

The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery. METHODS: Using the PubMed search engine, 327 original journal articles published from 1996 to July 2019 and related to the use of AI technologies in neurosurgery, were selected. The typical issues addressed by using AI were identified for each area of neurosurgery. RESULTS: The typical AI applications within each of the five main areas of neurosurgery (neuro-oncology, functional, vascular, spinal neurosurgery, and traumatic brain injury) were defined. CONCLUSION: The article highlights the main areas and trends in the up-to-date AI research in neurosurgery, which might be helpful in planning new scientific projects.

Memory Modulation Factors in Hippocampus Exposed to Radiation.

Although the key scene of the hippocampus in memory processes is obvious, the specificity of its participation in information processing is far from being established. Current advanced neuroimaging enables to operate with precise morphometric parameters. The aim of the study was to reveal fine memory rearrangements under mechanical impact on the hippocampus by a neoplasm and radiation exposure in the course of therapy. MATERIALS AND METHODS: We used a homogeneous sample of 28 patients with parasellar meningiomas adjacent to hippocampus. In 10 patients (5 with left-sided and 5 with right-sided meningiomas), the tumor was located near the hippocampus but exhibited no mechanical effect on it. In 18 patients (10 with left-sided and 8 with right-sided tumors), the neoplasm compressed the adjacent hippocampus. The control group consisted of 39 healthy subjects. All three groups were comparable in age, education, and gender characteristics. In order to control tumor growth, the patients underwent radiotherapy when the hippocampus involuntary was exposed to a dose comparable to that in the tumor (30 sessions with a single focal dose of 1.8 Gy, total dose - 54.0 Gy).Based on the literature data on hippocampus involved in mnestic processes, a special methodology to investigate memory was developed. Incorrect responses the subjects made when identifying previously memorized images were classified as neutralizing the novelty factor of an identified stimulus or as wrongly emphasizing its novelty. RESULTS: At the first observation point (before radiation therapy) all groups underwent a complete standardized neuropsychological examination and performed a battery of cognitive tests. The overall results of the tests assessing attention, memory, thinking processes, and neurodynamic indicators corresponded to standard values. A mild brain compression by the tumor without brain tissue destruction was not accompanied by focal neuropsychological symptoms and deficit manifestations in the cognitive sphere. However, as early as in the first observation point, the number of "pattern separation" errors in the clinical group was significantly higher than that in healthy subjects.The second observation point (immediately after radiotherapy) and the third observation point - 6 months after the treatment - showed that, in general, the patients' cognitive sphere condition was not deteriorating, and in a number of parameters was characterized by positive dynamics, apparently associated with some tumor reduction due to the therapy provided. However, the distribution of errors in the original method significantly changed. When previously memorized stimuli were recognized, the errors neutralizing the novelty factor of the evaluated stimulus increased, while the number of errors with overestimating the stimuli novelty decreased.All tendencies hypothetically (according to the published data) associated with the changes in functional activity of the hippocampus were more pronounced in the subgroup of patients with mechanical impact of the tumor on hippocampus. CONCLUSION: The continuous flow of impressions any person has at any moment of his activity is most likely marked by the hippocampus in a continuum "old-similar-new". The present study has shown that mechanical impact on the hippocampus combined with radiation exposure changes the range of assessments towards the prevailing labeling "old, previously seen, already known".

[Surgical treatment of drug-resistant epilepsy following hippocampal sclerosis]. / Khirurgicheskoe lechenie farmakorezistentnoi epilepsii pri skleroze gippokampa.

Surgery is an effective approach for drug-resistant temporal lobe epilepsy following hippocampal sclerosis. There is still no clear and unanimous opinion about advantages and disadvantages of certain surgical technique. MATERIAL AND METHODS: There were 103 surgical interventions in 101 patients. Females prevailed (1.45:1). Age of patients ranged from 16 to 56 years (median 28). Anteromedial temporal lobectomy and selective amygdaloghippocampectomy were performed in 49 (47.6%) and 54 (52.4%) patients, respectively. In the latter group, 30 patients were operated via a 14-mm burr hole-subtemporal approach. Postoperative outcomes were assessed using the Engel grading system. The follow-up period ranged from 2 to 8 years (median 4 years). RESULTS: By the 2nd year, Engel class I was observed in 74 (72%) patients, Engel II, III and IV - in 20 (19.4%), 6 (5.8%) and 3 (2.9%) patients, respectively. Engel class I was achieved after anteromedial temporal lobectomy in 68% of cases, selective amygdaloghippocampectomy via standard approaches in 75% of cases, amygdaloghippocampectomy via subtemporal burr hole approach - in 80% of cases. Neurocognitive impairments after anteromedial lobectomy and selective amygdaloghippocampectomy were similar. At the same time, mental disorders de novo prevailed in the group of anteromedial lobectomy (p<0.05). There were no severe visual field disorders after subtemporal burr-hole access. In other cases, these disorders occurred in 36.2% of patients (p<0.05). There were 8 (7.8%) postoperative complications: 5 (10.2%) - after anterior temporal lobectomy, 3 (5.5%) - after selective surgeries via standard approaches. There were no complications after burr-hole surgery. CONCLUSION: Selective amygdaloghippocampectomy is not inferior to anteromedial lobectomy. Moreover, this procedure is associated with a lower risk of complications and adverse events.