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

BackgroundThe 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.MethodsLungQuant 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.ResultsDespite 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.ConclusionsVisual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts

Magnetic Resonance with Diffusion and Dynamic Perfusion-Weighted Imaging in the Assessment of Early Chemoradiotherapy Response of Naso-Oropharyngeal Carcinoma

The purpose of this study was to differentiate post-chemoradiotherapy (CRT) changes from tumor persistence/recurrence in early follow-up of naso-oropharyngeal carcinoma on magnetic resonance (MRI) with diffusion (DWI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PWI). A total of 37 patients were assessed with MRI both for tumor staging and 4-month follow-up from ending CRT. Mean apparent diffusion coefficient (ADC) values, area under the curve (AUC), and K(trans) values were calculated from DWI and DCE-PWI images, respectively. DWI and DCE-PWI values of primary tumor (ADC, AUC, K(trans)(pre)), post-CRT changes (ADC, AUC, K(trans)(post)), and trapezius muscle as a normative reference before and after CRT (ADC, AUC, K(trans)(muscle pre) and (muscle post); AUC(post/muscle post):AUC(pre/muscle pre) (AUC(post/pre/muscle)); K(trans)(post/muscle post):K(trans)(pre/muscle pre) (K(trans)(post/pre/muscle)) were assessed. In detecting post-CRT changes, ADC(post) > 1.33 x 10(-3) mm(2)/s and an increase >0.72 x 10(-3) mm(2)/s and/or >65.5% between ADC(post) and ADC(pre) values (ADC(post-pre); ADC(post-pre%)) had 100% specificity, whereas hypointense signal intensity on DWIb800 images showed specificity 80%. Although mean AUC(post/pre/muscle) and K(trans)(post/pre/muscle) were similar both in post-CRT changes (1.10 +/- 0.58; 1.08 +/- 0.91) and tumor persistence/recurrence (1.09 +/- 0.11; 1.03 +/- 0.12), K(trans)(post/pre/muscle) values 1.20 suggested post-CRT fibrosis and inflammatory edema, respectively. In early follow-up of naso-oropharyngeal carcinoma, our sample showed that ADC(post) > 1.33 x 10(-3) mm(2)/s, ADC(post-pre%) > 65.5%, and ADC(post-pre) > 0.72 x 10(-3) mm(2)/s identified post-CRT changes with 100% specificity. K(trans)(post/pre/muscle) values less than 0.85 suggested post-CRT fibrosis, whereas K(trans)(post/pre/muscle) values more than 1.20 indicated inflammatory edema

Technological advances in body CT: a primer for beginners

Many technological advances have entered the clinical routine of Computed Tomography (CT) imaging. The new CT scanners have specific solutions in gantry design to bear the mechanical solicitations. The X-ray tubes have been improved for faster acquisitions at low radiation exposure, while the innovations in CT detectors provide a better image quality. The optimization of image quality and contrast, and the reduction of radiation dose, cannot be achieved without the implementation of adequate reconstruction software, such as Iterative Reconstructions (IR) and Artificial Intelligence (AI). In recent years, dual- energy (DECT) technology has expanded the indications of CT.In this narrative review, a panoramic overview of the technological novelties in CT imaging will be provided for optimal utilization of CT technology

Non-traumatic non-cardiovascular thoracic emergencies: role of imaging

Patients presenting to the emergency with thoracic symptoms could have a wide variety of causes, even if the traumatic and vascular causes are excluded. Therefore, the diagnosis is often a challenge for emergency physicians. Anamnesis, physical examination and laboratory testing need to be integrated with imaging to get a rapid diagnosis and to distinguish among the potential causes. This review discusses the role of diagnostic imaging studies in the emergency setting in patients with non-traumatic non-cardiovascular thoracic symptoms. The use of chest x-ray, bedside lung Ultrasound and Computed Tomography in the diagnosis and care of these patients have been reviewed as well as the common findings on imaging

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

Neural stem cells traffic functional mitochondria via extracellular vesicles

Neural stem cell (NSC) transplantation induces recovery in animal models of central nervous system (CNS) diseases. Although the replacement of lost endogenous cells was originally proposed as the primary healing mechanism of NSC grafts, it is now clear that transplanted NSCs operate via multiple mechanisms, including the horizontal exchange of therapeutic cargoes to host cells via extracellular vesicles (EVs). EVs are membrane particles trafficking nucleic acids, proteins, metabolites and metabolic enzymes, lipids, and entire organelles. However, the function and the contribution of these cargoes to the broad therapeutic effects of NSCs are yet to be fully understood. Mitochondrial dysfunction is an established feature of several inflammatory and degenerative CNS disorders, most of which are potentially treatable with exogenous stem cell therapeutics. Herein, we investigated the hypothesis that NSCs release and traffic functional mitochondria via EVs to restore mitochondrial function in target cells. Untargeted proteomics revealed a significant enrichment of mitochondrial proteins spontaneously released by NSCs in EVs. Morphological and functional analyses confirmed the presence of ultrastructurally intact mitochondria within EVs with conserved membrane potential and respiration. We found that the transfer of these mitochondria from EVs to mtDNA-deficient L929 Rho0 cells rescued mitochondrial function and increased Rho0 cell survival. Furthermore, the incorporation of mitochondria from EVs into inflammatory mononuclear phagocytes restored normal mitochondrial dynamics and cellular metabolism and reduced the expression of pro-inflammatory markers in target cells. When transplanted in an animal model of multiple sclerosis, exogenous NSCs actively transferred mitochondria to mononuclear phagocytes and induced a significant amelioration of clinical deficits. Our data provide the first evidence that NSCs deliver functional mitochondria to target cells via EVs, paving the way for the development of novel (a)cellular approaches aimed at restoring mitochondrial dysfunction not only in multiple sclerosis, but also in degenerative neurological diseases