Optochiasmatic cavernoma: Surgical treatment and outcomes.

OBJECTIVE: Optochiasmatic cavernoma is an extremely rare cerebral lesion. They account for approximately 1% of all cavernomas of the central nervous system. Reports on this pathology are limited. Abrupt visual deterioration is a common symptom of the disease. Treatment strategy and visual outcomes after different treatment approaches remain a subject for discussion. METHODS: Patients operated in a period 2005-2021 were analyzed in this study. All patients preoperatively underwent computed tomography (CT) scan, CT-angiography, and magnetic resonance imaging (MRI). Visual function of the patients was assessed pre-op, post-op and at the follow-up. Duration of visual dysfunction was noted as well. Surgical details were also extracted from medical notes. All patients were followed up, and control MRI was performed one month after operation. We assessed surgical series of optochiasmatic cavernomas published for last 10 years. Further comparative analysis with our data was performed. RESULTS: Five patients were included into this study. There were four men and one woman. Mean age comprised 33.8 years (range 20-48 years). Most patients were admitted to our hospital due to visual disturbances (80%). Visual function improved in four patients. Visual function was unchanged in one patient, lacking visual disturbancies pre-op. Complication developed in one patient. CONCLUSIONS: Optochiasmatic cavernomas are encountered extremely rare. Despite the use of contemporary diagnostic options, differential diagnosis remains challenging. Full diagnostic work-up is mandatory. After the diagnosis is made, surgical treatment should be considered first. Total microsurgical or endoscopic transsphenoidal removal of the optochiasmatic cavernoma is a relatively safe and effective treatment method facilitating improvement of visual function.

Neurosurgical skills analysis by machine learning models: systematic review.

Machine learning (ML) models are being actively used in modern medicine, including neurosurgery. This study aimed to summarize the current applications of ML in the analysis and assessment of neurosurgical skills. We conducted this systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched the PubMed and Google Scholar databases for eligible studies published until November 15, 2022, and used the Medical Education Research Study Quality Instrument (MERSQI) to assess the quality of the included articles. Of the 261 studies identified, we included 17 in the final analysis. Studies were most commonly related to oncological, spinal, and vascular neurosurgery using microsurgical and endoscopic techniques. Machine learning-evaluated tasks included subpial brain tumor resection, anterior cervical discectomy and fusion, hemostasis of the lacerated internal carotid artery, brain vessel dissection and suturing, glove microsuturing, lumbar hemilaminectomy, and bone drilling. The data sources included files extracted from VR simulators and microscopic and endoscopic videos. The ML application was aimed at classifying participants into several expertise levels, analysis of differences between experts and novices, surgical instrument recognition, division of operation into phases, and prediction of blood loss. In two articles, ML models were compared with those of human experts. The machines outperformed humans in all tasks. The most popular algorithms used to classify surgeons by skill level were the support vector machine and k-nearest neighbors, and their accuracy exceeded 90%. The "you only look once" detector and RetinaNet usually solved the problem of detecting surgical instruments - their accuracy was approximately 70%. The experts differed by more confident contact with tissues, higher bimanuality, smaller distance between the instrument tips, and relaxed and focused state of the mind. The average MERSQI score was 13.9 (from 18). There is growing interest in the use of ML in neurosurgical training. Most studies have focused on the evaluation of microsurgical skills in oncological neurosurgery and on the use of virtual simulators; however, other subspecialties, skills, and simulators are being investigated. Machine learning models effectively solve different neurosurgical tasks related to skill classification, object detection, and outcome prediction. Properly trained ML models outperform human efficacy. Further research on ML application in neurosurgery is needed.

Prediction of Postoperative Speech Dysfunction Based on Cortico-Cortical Evoked Potentials and Machine Learning.

The possibility of postoperative speech dysfunction prediction in neurosurgery based on intraoperative cortico-cortical evoked potentials (CCEP) might provide a new basis to refine the criteria for the extent of intracerebral tumor resection and preserve patients' quality of life. In this study, we aimed to test the quality of predicting postoperative speech dysfunction with machine learning based on the initial intraoperative CCEP before tumor removal. CCEP data were reported for 26 patients. We used several machine learning models to predict speech deterioration following neurosurgery: a random forest of decision trees, logistic regression, support vector machine with different types of the kernel (linear, radial, and polynomial). The best result with F1-score = 0.638 was obtained by a support vector machine with a polynomial kernel. Most models showed low specificity and high sensitivity (reached 0.993 for the best model). Our pilot study demonstrated the insufficient quality of speech dysfunction prediction by solely intraoperative CCEP recorded before glial tumor resection, grounding our further research of CCEP postresectional dynamics.

Neuromonitoring of the language pathways using cortico-cortical evoked potentials: a systematic review and meta-analysis.

Cortico-cortical evoked potentials (CCEPs) are a surge in activity of one cortical zone caused by stimulation of another cortical zone. Recording of CCEP may be a useful method of intraoperative monitoring of the brain pathways, particularly of the language-related tracts. We aimed to conduct a systematic review and meta-analysis, dedicated to the clinical question: Does the CCEP recording effectively predict the postoperative speech deficits in neurosurgical patients? We conducted language-restricted PubMed, Google Scholar, Scopus, and Cochrane database search for eligible studies of CCEP published until March 2021. There were 4 articles (3 case series and 1 case report), which met our inclusion/exclusion criteria. A total of 32 patients (30 cases of tumors and 2 cavernomas) included in the analysis were divided into two cohorts - quantitative and qualitative, in accordance with the method of evaluating changes in the amplitude of CCEP after the lesion resection and postoperative alterations in speech function. Quantitative variables were studied using the Spearman rank correlation coefficient. Categorical variables were compared in groups by Fisher's exact test. We found a strong positive correlation between the decrease in the N1 wave amplitude and the severity of postoperative speech deficits (quantitative cohort: r = 0.57, p = 0.01; qualitative cohort: p = 0.02). Thus, the CCEP method using the N1 wave amplitude as a marker enables to effectively predict postoperative speech outcomes. Nevertheless, the low level of evidence for the included works indicated the necessity for additional research on this issue.

Calamus: A Pen for Microneurosurgical Training.

Microneurosurgery is Sisyphean labor: dexterity hardly comes and easily goes. The only way to stay ahead is to constantly train. However, it requires a special working place and a lot of expensive equipment, which dramatically decreases the training availability. The author proposes Calamus - a novel tool, which may solve this problem. Calamus is a pen with the anatomy of microneurosurgical instruments. It includes a single bayonet and round handle, a curved tip, and a replaceable refill. Given a lot of similarities between the techniques of microneurosurgery and calligraphy (fine hand movements, working pose, method of controlling the instruments, space management, breathing, psychological state), writing or drawing something using Calamus simulates a crucial neurosurgical skill - working in a deep and narrow operative field (skull base, brainstem, etc.), wherein the quality of technique is objectively reflected by the inky trail. This simple and low-cost instrument can make the microneurosurgical training available for everyone, at every time and place.

Brain Mesh: Technical Note.

BACKGROUND: Standard electrostimulation cortical mapping includes application of electrical current to the explored areas through an electrode and marking of functional zones by means of paper tags with different symbols. This approach has several disadvantages. First, the electrode is moved randomly. It leads to overlooking of some zones, which causes mapping deficiency, and restimulation of others, which can trigger epileptic seizures. Second, the tags easily shift and close the marked structures. We describe a new simple device that provides precise cortical mapping without indicated problems and the technique to apply it. METHODS: The device is a flexible polymer mesh with square pores of a certain size. The neurosurgeon applies the mesh onto the brain cortex and sequentially stimulates it through the pores. The functional areas are labeled. Pores corresponding to the lesion are cut out, and the lesion is removed through the cutout without removing the mesh. After operation, the mesh is removed. RESULTS: Using this technique, we operated on a patient with a glioma located near the primary motor cortex. The accessible cortical area was accurately mapped, and the tumor was resected without any complications. The mesh allowed us to significantly streamline the mapping process. CONCLUSIONS: Our case illustrates that the proposed invention can be successfully used in neurosurgical operations for precise electrostimulation mapping of the brain cortex.

Quadrupole Correction: From Molecular Electrostatic Potential to Free Energies of Halogen Bonding.

Recently several molecular mechanics models of halogen bonding have been published. They describe the electrostatic potential anisotropy near the heavy halogen atom (known as a σ-hole) in different ways, ranging from an all-atom multipole expansion to a single positive extra-point charge. However, the question of a reasonable balance between the accuracy and the simplicity of the model remains open. In this work, we introduce the simplistic RESPQ electrostatics model built on the RESP charges complemented with fixed atomic quadrupoles. We show that it: (1) correctly describes the MEP anisotropy of aromatic halogen atoms, (2) improves the description of the halogen-water interaction energies in both halogen and hydrogen bonding cases, (3) provides an excellent estimation of solvation free energy differences of aromatic halogens, and (4) is compatible (with the help of multipole charge cluster approximation) with contemporary molecular modeling packages.

Second Epoch VLBA Calibrator Survey Observations - VCS-II.

Six very successful VLBA calibrator survey campaigns were run between 1994 and 2007 to build up a large list of compact radio sources with positions precise enough for use as VLBI phase reference calibrators. We report on the results of a second epoch VLBA Calibrator Survey campaign (VCS-II) in which 2400 VCS sources were re-observed at X and S bands in order to improve the upcoming third realization of the International Celestial Reference Frame (ICRF3) as well as to improve their usefulness as VLBI phase reference calibrators. In this survey, some 2062 previously detected sources and 324 previously undetected sources were detected and revised positions are presented. Average position uncertainties for the re-observed sources were reduced from 1.14 and 1.98 mas to 0.24 and 0.41 mas in RA and Declination, respectively, or by nearly a factor of 5. Minimum detected flux values were approximately 15 and 28 mJy in X and S bands, respectively, and median total fluxes are approximately 230 and 280 mJy. The vast majority of these sources are flat-spectrum sources, with ~82% having spectral indices greater than -0.5.

Perspectives of Halogen Bonding Description in Scoring Functions and QSAR/QSPR: Substituent Effects in Aromatic Core.

Halogen bonding (XB) is a new promising interaction pattern in medicinal chemistry. It has predominantly electrostatic nature - high electrostatic potential anisotropy. However to fully unleash the potential of XB in rational drug design fast and robust empirical methods of XB description should be developed. Current approaches rely heavily on ab initio calculation for each molecule studied. Thus fast prediction of electrostatic parameters for description of XB for arbitrary organic molecules is of paramount importance to promptly establish QSAR/QSPR, virtual screening and molecular docking pipelines suitable for today's agile development requirements. The two most promising approaches to describe anisotropic electrostatic models - the extra point (EP) charge model and the multipole expansion (ME) model - were studied on their ability (1) to describe ab initio molecular electrostatic potential (MEP) and (2) to produce parameters that can be predicted for each molecule empirically rather than estimated via ab initio calculations. The reference ab initio MEP was calculated for a set of 730 substituted halobenzenes. Parameters for anisotropic electrostatics of both empirical models (EP and ME) studied were extracted from ab initio MEP. The FreeWilson and Hansch type QSPR models relating XB parameters with aromatic substituents were built and analyzed, providing the guidelines for further development.