Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases

Objectives To compare the lung CT volume (CTvol) and pulmonary function tests in an interstitial lung disease (ILD) population. Then to evaluate the CTvol loss between idiopathic pulmonary fibrosis (IPF) and non-IPF and explore a prognostic value of annual CTvol loss in IPF. Methods We conducted in an expert center a retrospective study between 2005 and 2018 on consecutive patients with ILD. CTvol was measured automatically using commercial software based on a deep learning algorithm. In the first group, Spearman correlation coefficients (r) between forced vital capacity (FVC), total lung capacity (TLC), and CTvol were calculated. In a second group, annual CTvol loss was calculated using linear regression analysis and compared with the Mann–Whitney test. In a last group of IPF patients, annual CTvol loss was calculated between baseline and 1-year CTs for investigating with the Youden index a prognostic value of major adverse event at 3 years. Univariate and log-rank tests were calculated. Results In total, 560 patients (4610 CTs) were analyzed. For 1171 CTs, CTvol was correlated with FVC (r: 0.86) and TLC (r: 0.84) (p < 0.0001). In 408 patients (3332 CT), median annual CTvol loss was 155.7 mL in IPF versus 50.7 mL in non-IPF (p < 0.0001) over 5.03 years. In 73 IPF patients, a relative annual CTvol loss of 7.9% was associated with major adverse events (log-rank, p < 0.0001) in univariate analysis (p < 0.001). Conclusions Automated lung CT volume may be an alternative or a complementary biomarker to pulmonary function tests for the assessment of lung volume loss in ILD. Key Points • There is a good correlation between lung CT volume and forced vital capacity, as well as for with total lung capacity measurements (r of 0.86 and 0.84 respectively, p < 0.0001). • Median annual CT volume loss is significantly higher in patients with idiopathic pulmonary fibrosis than in patients with other fibrotic interstitial lung diseases (155.7 versus 50.7 mL, p < 0.0001). • In idiopathic pulmonary fibrosis, a relative annual CT volume loss higher than 9.4% is associated with a significantly reduced mean survival time at 2.0 years versus 2.8 years (log-rank, p < 0.0001).

CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning

Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizing patients by severity of the disease. In this article we adopted an approach based on using an ensemble of deep convolutional neural networks for segmentation of slices of lung CT scans. Using our models, we are able to segment the lesions, evaluate patients’ dynamics, estimate relative volume of lungs affected by lesions, and evaluate the lung damage stage. Our models were trained on data from different medical centers. We compared predictions of our models with those of six experienced radiologists, and our segmentation model outperformed most of them. On the task of classification of disease severity, our model outperformed all the radiologists.

COVID-19-Associated Pulmonary Aspergillosis in Russia

We studied the risk factors, etiology, clinical features and the effectiveness of therapy of COVID-19-associated pulmonary aspergillosis (CAPA) in adult patients. In this retrospective study, we included 45 patients with proven (7%) and probable (93%) CAPA. The ECMM/ISHAM, 2020 criteria were used to diagnose CAPA. A case-control study was conducted to study the risk factors of CAPA; the control group included 90 adult COVID-19 patients without IA. In CAPA patients, the main underlying diseases were diabetes mellitus (33%), and hematological and oncological diseases (31%). The probability of CAPA developing significantly increased with lymphocytopenia >10 days (OR = 8.156 (3.056–21.771), p = 0.001), decompensated diabetes mellitus (29% vs. 7%, (OR = 5.688 (1.991–16.246), p = 0.001)), use of glucocorticosteroids (GCS) in prednisolone-equivalent dose > 60 mg/day (OR = 4.493 (1.896–10.647), p = 0.001) and monoclonal antibodies to IL-1ß and IL-6 (OR = 2.880 (1.272–6.518), p = 0.01). The main area of localization of CAPA was the lungs (100%). The clinical features of CAPA were fever (98% vs. 85%, p = 0.007), cough (89% vs. 72%, p = 0.002) and hemoptysis (36% vs. 3%, p = 0.0001). Overall, 71% of patients were in intensive care units (ICU) (median—15.5 (5–60) days), mechanical ventilation was used in 52% of cases, and acute respiratory distress syndrome (ARDS) occurred at a rate of 31%. The lung CT scan features of CAPA were bilateral (93%) lung tissue consolidation (89% vs. 59%, p = 0.004) and destruction (47% vs. 1%, p = 0.00001), and hydrothorax (26% vs. 11%, p = 0.03). The main pathogens were A. fumigatus (44%) and A. niger (31%). The overall survival rate after 12 weeks was 47.2%.

Combined presentation of severe pulmonary COVID-19 and multisystem inflammatory syndrome in children (MIS-C): two reported Egyptian pediatric cases

Background Initial reports from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic described children as being less susceptible to coronavirus disease (COVID-19) than adults. Later on, a severe and novel pediatric disorder termed multisystem inflammatory syndrome in children (MIS-C) emerged. Pediatric patients with SARS-CoV-2 are at risk for critical illness with severe pulmonary COVID-19 and MIS-C. Both are described as two distinct conditions, and the differentiation between them was the scope of many studies. In this report from Egypt, we will describe two unique pediatric cases presented by combined manifestations of severe pulmonary COVID-19 and MIS-C. Case presentation Two patients presented with severe pulmonary COVID-19 evident by pulmonary symptoms, signs, and advanced CO-RADS stage in lung CT were simultaneously fulfilling the clinical criteria of MIS-C including fever, multi-system affection, increased inflammatory markers in addition to the proved COVID-19 by positive serologic tests for SARS-CoV-2 but PCR was negative. Both patients responded well to immune-modulation therapy by IVIG and steroids and discharged well under closed follow-up. Conclusions Although it is debatable to present simultaneously, MIS-C should be considered in patients presenting with typical clinical findings and concerns for pulmonary COVID-19 once the criteria for MIS-C diagnosis is fulfilled. Starting treatment without delay can favor better prognosis.

Enhancement of digital chest images using a modified Sobel edge detection algorithm

Computed tomography (CT) images are an essential factor in the diagnosing procedure for various diseases affecting the internal organs. Edge detection can be used for the appropriate enhancement of the lung CT scan images for the diagnosis of the various interstitial lung diseases (ILD). In order to solve the issues of edge detection provided by the traditional Sobel operator, the paper proposes a Sobel 12D edge detection algorithm which uses the additional direction templates for the better identification of the edge details. First, the vertical and horizontal directions available in the traditional Sobel operator are extended to few more directions (a total of 12 directions) which enhances the edge extraction ability. Next part, compute the edge detected image using the Sobel 12D, Laplace, Prewitt, Robert’s Cross and Scharr operators for edge detection separately. It is followed by image fusion method which optimizes the edge detection by combining the edge detected images obtained using the Sobel 12D approach and the Laplace operator. The experimental results shows that the proposed algorithms generates a better detection of the edges than the other edge detection operators.

Optimized lung tumor diagnosis system using enhanced version of crow search algorithm, Zernike moments, and support vector machine

Early detection of lung tumors is so important to heal this disease in the initial steps. Automatic computer-aided detection of this disease is a good method for reducing human mistakes and improving detection precision. The major concept here is to propose the best CAD system for lung tumor detection. In the presented technique, after pre-processing and segmentation of the lung area, its features including different orders of Zernike moments have been extracted. After features extraction, they have been injected into an optimized version of Support Vector Machine (SVM) for final diagnosis. The optimization of the SVM is based on an enhanced design of the Crow Search Algorithm (ECSA). For validating the proposed method, it was applied to three datasets including Lung CT-Diagnosis, TCIA, and RIDER Lung CT collection, and the results are validated by comparing with three state-of-the-art methods including Walwalker method, Mon method, and Naik method to indicate the system superiority toward the compared methods. The system is also analyzed based on different orders of Zernike moment to select the best order. The final results indicate that the suggested method has a suitable accuracy for diagnosing lung cancer.