Quantitative Computed Tomography and Response to Pronation in COVID-19 ARDS.

Background: The use of prone position (PP) has been widespread during the COVID-19 pandemic. Whereas it has demonstrated benefits, including improved oxygenation and lung aeration, the factors influencing the response in terms of gas exchange to PP remain unclear. In particular, the association between baseline quantitative computed tomography (CT) scan results and gas exchange response to PP in invasively ventilated subjects with COVID-19 ARDS is unknown.

Language lateralization mapping (reversibly) masked by non-dominant focal epilepsy: a case report.

Language lateralization in patients with focal epilepsy frequently diverges from the left-lateralized pattern that prevails in healthy right-handed people, but the mechanistic explanations are still a matter of debate. Here, we debate the complex interaction between focal epilepsy, language lateralization, and functional neuroimaging techniques by introducing the case of a right-handed patient with unaware focal seizures preceded by aphasia, in whom video-EEG and PET examination suggested the presence of focal cortical dysplasia in the right superior temporal gyrus, despite a normal structural MRI. The functional MRI for language was inconclusive, and the neuropsychological evaluation showed mild deficits in language functions.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

Background: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.

Adaptive Staged-Dose Gamma Knife Radiosurgery for the Treatment of Large Brain Metastases: Report of 40 Consecutive Cases and Analysis of Literature.

Background: Brain metastases are the most common brain tumors, being one of the most frequent neurological complications of systemic cancer and an important cause of morbidity and mortality. Stereotactic radiosurgery is efficacious and safe in the treatment of brain metastases, with good local control rates and low adverse effects rate. Large brain metastases present some issues in balancing local control and treatment-related toxicity.

Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.

Background: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19).

Methods: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80).

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 ( = 647) and other respiratory viruses ( = 384). The model was trained with 811 CT scans, while 220 CT scans ( = 151 COVID-19; = 69 non-COVID-19) were used for independent validation.

Tracing in vivo the dorsal loop of the optic radiation: convergent perspectives from tractography and electrophysiology compared to a neuroanatomical ground truth.

The temporo-parietal junction (TPJ) is a cortical area contributing to a multiplicity of visual, language-related, and cognitive functions. In line with this functional richness, also the organization of the underlying white matter is highly complex and includes several bundles. The few studies tackling the outcome and neurological burdens of surgical operations addressing TPJ document the presence of language disturbances and visual field damages, with the latter hardly recovered in time.

Intraoperative Corticocortical Evoked Potentials for Language Monitoring in Epilepsy Surgery.

Objective: To evaluate the applicability of corticocortical evoked potentials (CCEP) for intraoperative monitoring of the language network in epilepsy surgery under general anesthesia. To investigate the clinical relevance on language functions of intraoperative changes of CCEP recorded under these conditions.

Methods: CCEP monitoring was performed in 14 epileptic patients (6 females, 4 children) during resections in the left perisylvian region under general anesthesia.

Gamma Knife Radiosurgery for Treatment of Brain Metastases during the COVID-19 Outbreak.

Introduction: The WHO declared 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a public health emergency of international concern. The National and Regional Health System has been reorganized, and many oncological patients died during this period or had to interrupt their therapies. This study summarizes a single-centre experience, during the COVID-19 period in Italy, in the treatment of brain metastases with Gamma Knife stereotactic radiosurgery (GKRS).

Can sinogram-affirmed iterative reconstruction improve the detection of small hypervascular liver nodules with dual-energy CT?

Objective: To optimize a dual-energy computed tomographic protocol with sinogram-affirmed iterative reconstruction algorithms for improving small nodules detection.

Methods: The raw data of a dual-energy computed tomographic arterial acquisition of a cirrhotic patient were reconstructed with a standard filtered back projection (B20f) and 3 iterative (I26, I30, I31) kernels with different strength (S3-S5). The 80-kilovolt (peak) (kVp) and the linear blended (DE_0.