Immunohistochemical study of a canine neurofibroma

Accurate diagnostic approaches to differentiate peripheral nerve sheet tumours from others have not been firmly established. The aim of this case report was to diagnose neurofibroma using a combination of diagnostic imaging, histopathology and immunohistochemistry, which were applied to a canine neurofibroma arising in the left mandible. The tumour was surgically excised and examined histologically. Round or spindle cells, with elongated, dense and homogenous chromatin and pale cytoplasm typical of Schwann cells in an abundant fibromyxomatous stroma, with ruby collagen fibres were seen. Immunohistochemistry demonstrated that S-100 and vimentin were more than 70% positive. Neurofibroma may therefore be recognisable using markers such as S-100 and vimentin

Position-dependent hemodynamic changes in neurosurgery patients: A narrative review

Patient positioning is one of the milestones of neurological surgery, which significantly affects the surgical outcome and requires a multidisciplinary approach. Several positions are applied in neurosurgical interventions, including supine, lateral, park bench, prone, sitting, and semi-sitting. In this regard, different positions could lead to changes in intracranial pressure (ICP) and cerebral hemodynamics. Cerebral autoregulation (CA) is a safeguard for adequate cerebral perfusion that keeps cerebral blood flow (CBF) constant in a variable range of cerebral perfusion pressures (CPP). The clinical management of the dysfunction in CBF is still challenging due to the narrow range of cerebral self-regulation, especially in neurovascular and oncological neurosurgery. Moreover, patient positioning might predispose the patient to various intra- or postoperative complications. Changes in hemodynamics and cardiovascular function as a result of patient positioning have also been reported previously. The ICP monitoring aids us in managing surgical conditions and preventing sudden complications in neurosurgery, such as venous air embolism (VAE), hypotension, and arrhythmias. Other positional-related complications in neurosurgery are cerebral bleeding, venous and paradoxical air embolism, and pneumocephalus. Therefore, the present review aims to discuss different aspects of patient positioning in neurosurgical procedures, such as its potential impacts on cerebral hemodynamics and surgical outcomes

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p