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. The present study aimed to compare baseline quantitative CT results between subjects responding to PP in terms of oxygenation or CO2 clearance and those who did not. METHODS: This was a single-center, retrospective observational study including critically ill, invasively ventilated subjects with COVID-19-related ARDS admitted to the ICUs of Niguarda Hospital between March 2020-November 2021. Blood gas samples were collected before and after PP. Subjects in whom the PaO2 /FIO2 increase was ≥ 20 mm Hg after PP were defined as oxygen responders. CO2 responders were defined when the ventilatory ratio (VR) decreased during PP. Automated quantitative CT analyses were performed to obtain tissue mass and density of the lungs. RESULTS: One hundred twenty-five subjects were enrolled, of which 116 (93%) were O2 responders and 51 (41%) CO2 responders. No difference in quantitative CT characteristics and oxygen were observed between responders and non-responders (tissue mass 1,532 ± 396 g vs 1,654 ± 304 g, P = .28; density -544 ± 109 HU vs -562 ± 58 HU P = .42). Similar findings were observed when dividing the population according to CO2 response (tissue mass 1,551 ± 412 g vs 1,534 ± 377 g, P = .89; density -545 ± 123 HU vs -546 ± 94 HU, P = .99). CONCLUSIONS: Most subjects with COVID-19-related ARDS improved their oxygenation at the first pronation cycle. The study suggests that baseline quantitative CT scan data were not associated with the response to PP in oxygenation or CO2 in mechanically ventilated subjects with COVID-19-related ARDS.

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 bilateral stereo-EEG was proposed confirming the right superior temporal gyrus origin of seizures, revealing how ictal aphasia emerged only once seizures propagated to the left superior temporal gyrus and confirming, by cortical mapping, the left lateralization of the posterior language region. Stereo-EEG-guided radiofrequency thermocoagulations of the (right) focal cortical dysplasia not only reduced seizure frequency but led to the normalization of the neuropsychological assessment and the "restoring" of a classical left-lateralized functional MRI pattern of language. This representative case demonstrates that epileptiform activity in the superior temporal gyrus can interfere with the functioning of the contralateral homologous cortex and its associated network. In the case of presurgical evaluation in patients with epilepsy, this interference effect must be carefully taken into consideration. The multimodal language lateralization assessment reported for this patient further suggests the sensitivity of different explorations to this interference effect. Finally, the neuropsychological and functional MRI changes after thermocoagulations provide unique cues on the network pathophysiology of focal cortical dysplasia and the role of diverse techniques in indexing language lateralization in complex scenarios.

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. METHODS: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

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. Objective: Demonstrating adaptive staged-dose Gamma Knife radiosurgery (ASD-GKRS) being a safe and effective treatment for large brain metastases. Materials and Methods: We retrospectively analyzed our series of patients treated with two-stage Gamma Knife radiosurgery for large brain metastases in [BLINDED], between February 2018 and May 2020. Results: Forty patients with large brain metastases underwent adaptive staged-dose Gamma Knife radiosurgery, with median prescription dose of 12 Gy and a median interval between stages of 30 days. At three-month follow-up, the survival rate was 75.0% with a local control rate of 100%. At six-month follow-up, the survival rate was 75.0% with a local control rate of 96.7%. The mean volume reduction was 21.81 cm3 (16.76-26.86; 95% CI). The difference between baseline volume and six-month follow-up volume was statistically significant. Conclusions: Adaptive staged-dose Gamma Knife radiosurgery is a safe, non-invasive and effective treatment for brain metastases, with a low rate of side effects. Large prospective trials are needed to strengthen data obtained about the effectiveness and safety of this technique in managing large brain metastases.

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). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.

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 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.

Validation of SART 3.5D algorithm for cerebrovascular dynamics and artery versus vein classification in presurgical 3D digital subtraction angiographies.

Classification of arteries and veins in cerebral angiograms can increase the safety of neurosurgical procedures, such as StereoElectroEncephaloGraphy, and aid the diagnosis of vascular pathologies, as arterovenous malformations. We propose a new method for vessel classification using the contrast medium dynamics in rotational digital subtraction angiography (DSA). After 3D DSA and angiogram segmentation, contrast enhanced projections are processed to suppress soft tissue and bone structures attenuation effect and further enhance the CM flow. For each voxel labelled as vessel, a time intensity curve (TIC) is obtained as a linear combination of temporal basis functions whose weights are addressed by simultaneous algebraic reconstruction technique (SART 3.5D), expanded to include dynamics. Each TIC is classified by comparing the areas under the curve in the arterial and venous phases. Clustering is applied to optimize the classification thresholds. On a dataset of 60 patients, a median value of sensitivity (90%), specificity (91%), and accuracy (92%) were obtained with respect to annotated arterial and venous voxels up to branching order 4-5. Qualitative results are also presented about CM arrival time mapping and its distribution in arteries and veins respectively. In conclusion, this study shows a valuable impact, at no protocol extra-cost or invasiveness, concerning surgical planning related to the enhancement of arteries as major organs at risk. Also, it opens a new scope on the pathophysiology of cerebrovascular dynamics and its anatomical relationships.

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. Electrode strips were placed on the anterior language area (AL) and posterior language area (PL), identified by structural and functional magnetic resonance imaging. Single-pulse electric stimulations were delivered to pairs of adjacent contacts in a bipolar fashion. During resection, we monitored the integrity of the dorsal language pathway by stimulating either AL by recording CCEP from PL or vice versa, depending on stability and reproducibility of CCEP. We evaluated the first negative (N1) component of CCEP before, during, and after resection. RESULTS: All procedures were successfully completed without adverse events. The best response was obtained from AL during stimulation of PL in 8 patients and from PL during stimulation of AL in 6 patients. None of 12 patients with a postresection N1 amplitude decrease of 0%-15% from baseline presented postoperative language impairment. Decreases of 28% and 24%, respectively, of the N1 amplitude were observed in 2 patients who developed transient postoperative speech disturbances. CONCLUSIONS: The application of CCEP monitoring is possible and safe in epilepsy surgery under general anesthesia. Putative AL and PL can be identified using noninvasive presurgical neuroimaging. Decrease of N1 amplitude >15% from baseline may predict postoperative language deficits.

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). METHODS: We retrospectively analysed our series of patients with brain metastases who underwent GKRS at the Niguarda Hospital from February 24 to April 24, 2020. RESULTS: We treated 30 patients with 66 brain metastases. A total of 22 patients came from home and 8 patients were admitted to the emergency room for urgent neurological symptoms. Duration of stay was limited to 0-1 day in 17 patients. We chose to treat a cluster of 9 patients, whose greater lesion exceeded 10 cm3, with 2-stage modality GKRS to minimize tumour recurrence and radiation necrosis. CONCLUSION: Due to the COVID-19 pandemic, the whole world is at a critical crossroads about the use of health care resources. During the COVID-19 outbreak, the deferral of diagnostic and therapeutic procedures and a work backlog in every medical specialty are the natural consequences of reservation of resources for COVID-19 patients. GKRS improved symptoms and reduced the need for open surgeries, allowing many patients to continue their therapeutic path and sparing beds in ICUs. Neurosurgeons have to take into account the availability of stereotactic radiosurgery to reduce hospital stay, conciliating safety for patients and operators with the request for health care coming from the oncological patients and their families.

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_0.5) images (80-140 kVp) were analyzed. For each series, 8-subcentimeter low-contrast lesions were simulated within the liver. Four radiologists performed a detectability test and rated the image quality (5-point scales) in all images. RESULTS: The sensitivity increased from 31% (B20f) to 87.5% with sinogram-affirmed iterative reconstruction S5 kernels without a difference between 80-kVp and DE_0.5 series (W test, P = 0.062). The highest image quality rating was 3.8 (B20 DE_0.5), without difference from DE_0.5 I30-S5 and I26-S3. CONCLUSIONS: Iterative reconstructions increase the sensitivity for detecting abdominal lesions, even in the 80-kVp series. The kernel I30-S5 was considered the best.