Pulmonary Computed Tomography Research Articles

Computed tomography imaging and clinical significance of bacterium-positive pulmonary tuberculosis complicated with diabetes.

The increasing prevalence of tuberculosis (TB) and diabetes on a global scale poses a significant health challenge, particularly due to their co-occurrence, which amplifies the severity, recurrence and mortality rates associated with both conditions. This highlights the need for further investigation into their inter-relationship. To explore the computed tomography (CT) imaging and clinical significance of bacterium-positive pulmonary TB (PTB) combined with diabetes. There were 50 patients with bacterium-positive PTB and diabetes, and 50 with only bacterium-positive PTB. The latter were designated as the control group. The CT imaging of the two groups of patients was compared, including lesion range, shape, density and calcification. No significant differences were observed in age, gender, smoking and drinking history, high blood pressure, hyperlipidemia and family genetic factors between the groups. However, compared to the patients diagnosed solely with simple bacterium-positive PTB, those with concurrent diabetes showed a wider range of lesions and more complex and diverse morphology on CT images. Among them, intrapulmonary tuberculosis lesions were often accompanied by manifestations of pulmonary infection, such as cavity formation and bronchiectasis. At the same time, diabetes-related signs were often seen on CT images, such as pulmonary infection combined with diabetic pulmonary lesions. Logistic regression analysis identified age and medical history as significant factors influencing the degree of pulmonary infection and CT imaging outcomes in patients with both TB and diabetes. This suggests that older age and specific medical histories may increase the risk or severity of pulmonary damage in these patients. CT imaging reveals more complex lesions in PTB patients with diabetes, emphasizing the need for careful evaluation and comprehensive analysis to enhance diagnostic accuracy.

Size-matching in lung transplantation by computed tomography for end-stage interstitial disease: a retrospective analysis.

Size matching between donors and recipients is a major issue in lung transplantation (LTx), especially in patients with restrictive lung disease (RLD). This study aims to evaluate computed tomography (CT) as an additional method for defining the total lung capacity (TLC) in patients with end-stage interstitial disease awaiting LTx. Clinical data and CT scans from patients who underwent a first LTx from January 2014 to July 2018 in Bichat Hospital, Paris, were prospectively included in a database. CT TLC (ctTLC) was retrospectively calculated after semi-automatic contouring of the parenchyma and compared with measured TLC (mTLC) and predicted TLC (pTLC) values. The study group included 89 patients (male:female =68:21; mean age, 59.5±10.0 years). The time between pulmonary function tests (PFTs) and CT scan was 162±270 days [median, 67 days; interquartile range (IQR), 0-233 days]. ctTLC was inferior to mTLC and pTLC (respectively 2,979±1,001 mL, 3,530±1,077 and 6,381±955 mL, P<0.001). The relative difference between CT lung volume (ctLV) and measured lung volume (mLV) was higher on the left than on the right side (25.4% vs. 16.3%, respectively, P=0.11). After exclusion of two outliers, we found a significant correlation between ctTLC and mTLC (r=0.762, P<0.001). CT volume is a feasible method to assess TLC in patients with end-stage interstitial disease awaiting LTx. This study highlights potential size-mismatch for graft selection before LTx and opens the perspective of a prospective trial evaluating impact of size-matching by donor-recipient (D-R) ctTLC ratio on postoperative outcomes.

Increased adiposity-to-muscle ratio and severity of sinusitis affect quality of life in asthma: Computed tomographic analysis

BackgroundDeteriorated sinusitis and increased adiposity relative to muscle mass may affect the quality of life(QoL) in patients with asthma. However, whether these effects are observed regardless of intra-pulmonary pathology is unknown. ObjectivesTo evaluate the correlation of the cross-sectional ratio of abdominal visceral fat (VF) to erector spinae muscle (ESM) and sinus findings based on Lund-Mackey scoring system (LMS) on computed tomography (CT) with the impaired score of the Asthma Quality of Life Questionnaire (AQLQ), regardless of airway and parenchymal disease, in patients with asthma. MethodsParticipants from the Hokkaido-based severe asthma cohort who had completed AQLQ and CT examination at the entry. The participants were divided into high (highest) and low (other quartiles) groups based on the extra-pulmonary indices. The multivariate analysis examined the association of VF/ESM for the adiposity-to-muscle ratio, LMS with AQLQ, after adjusting for the airway fractal dimension for airway index, %low attenuation volume for parenchymal index. ResultsNo significant differences were observed in VF/ESM and LMS in terms of sex. The AQLQ score in the high VF/ESM group and high LMS group was lower than those in low VF/ESM group and low LMS group (N:Male/Female, 63/100). High VF/ESM (estimate [95% confidence interval], (-0.43[-0.61,-0.25]) and high LMS scores (-0.22(-0.41, -0.03)) were associated with low AQLQ scores, adjusted for age, body mass index, smoking status, blood eosinophil counts, and intra pulmonary CT indices. ConclusionsIncreased VF relative to ESM mass and high LMS may deteriorate asthma related QoL, regardless of intra-pulmonary disease.

Pulmonary CT perfusion robustly measures cardiac output in the context of multilevel pulmonary occlusion: a porcine study

BackgroundTo validate pulmonary computed tomography (CT) perfusion in a porcine model by invasive monitoring of cardiac output (CO) using thermodilution method.MethodsAnimals were studied at a single center, using a Swan-Ganz catheter for invasive CO monitoring as a reference. Fifteen pigs were included. Contrast-enhanced CT perfusion of the descending aorta and right and left pulmonary artery was performed. For variation purposes, a balloon catheter was inserted to block the contralateral pulmonary vascular bed; additionally, two increased CO settings were created by intravenous administration of catecholamines. Finally, stepwise capillary occlusion was performed by intrapulmonary arterial injection of 75-μm microspheres in four stages. A semiautomatic selection of AFs and a recirculation-aware tracer-kinetics model to extract the first-pass of AFs, estimating blood flow with the Stewart-Hamilton method, was implemented. Linear mixed models (LMM) were developed to calibrate blood flow calculations accounting with individual- and cohort-level effects.ResultsNine of 15 pigs had complete datasets. Strong correlations were observed between calibrated pulmonary (0.73, 95% confidence interval [CI] 0.6–0.82) and aortic blood flow measurements (0.82, 95% CI, 0.73–0.88) and the reference as well as agreements (± 2.24 L/min and ± 1.86 L/min, respectively) comparable to the state of the art, on a relatively wide range of right ventricle-CO measurements.ConclusionsCT perfusion validly measures CO using LMMs at both individual and cohort levels, as demonstrated by referencing the invasive CO.Relevance statementPossible clinical applications of CT perfusion for measuring CO could be in acute pulmonary thromboembolism or to assess right ventricular function to show impairment or mismatch to the left ventricle.Key points• CT perfusion measures flow in vessels.• CT perfusion measures cumulative cardiac output in the aorta and pulmonary vessels.• CT perfusion validly measures CO using LMMs at both individual and cohort levels, as demonstrated by using the invasive CO as a reference standard.Graphical

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Pulmonary Computed Tomography Screening Frequency in Primary Antibody Deficiency

BackgroundPatients with primary antibody deficiency (PAD) frequently suffer from pulmonary complications, associated with severe morbidity and mortality. Hence, regular pulmonary screening by computed tomography (CT) scanning is advised. However, predictive risk factors for pulmonary morbidity are lacking. ObjectiveTo identify PAD patients at risk for pulmonary complications necessitating regular CT screening. MethodsA retrospective longitudinal cohort study of PAD patients (median follow-up 7.4 (2.3 – 14.8) years). CTs were scored using the modified Brody-II scoring system. Clinical and laboratory parameters were retrospectively collected. Potential risk factors were identified by univariate analysis when p<0.2 and confirmed by multivariable logistic regression when p<0.05. ResultsThe following independent risk factors for progression of airway disease (AD) were identified: 1) diagnosis of X-linked agammaglobulinemia (XLA), 2) recurrent airway infections (2.5/year), 3) presence of AD at baseline. Signs of AD progression were detected in 5/11 XLA patients and in 17/80 of the other PAD patients. Of the patients who progressed, 17/22 had preexistent AD scores ≥7.0%. Increased AD scores were related to poorer FEV1 values and chronic cough. CVID and increased CD4 effector/memory cells were risk factors for an interstitial lung disease (ILD) score ≥13.0%. ILD ≥13.0% occurred in 12/80 patients. Signs of ILD progression were detected in 8/80 patients and 4/8 patients showing progression had preexistent ILD scores ≥13.0%. ConclusionWe identified risk factors that distinguished PAD patients at risk for airway disease and interstitial lung disease presence and progression which could guide future screening frequency, however independent and preferably prospective validation is needed.

Indicators for Hospitalization in Acute Pulmonary Embolism: Uncover the Association Between D-dimer Levels, Thrombus Volume and Radiomics

The advent of advanced computed tomography (CT) technology and the field of radiomics has opened up new avenues in diagnostic assessments. Increasingly, there is substantial evidence advocating for the incorporation of quantitative imaging biomarkers in the clinical decision-making process.This study aimed to examine the correlation between D-dimer levels and thrombus size in acute pulmonary embolism (PE) combining dual-energy CT (DECT) and radiomics and to investigate the diagnostic utility of a machine learning classifier based on dual-energy computed tomography (DECT) radiomics for identifying patients with a complicated course, defined as at least hospitalization at IMC. The study was conducted including 136 participants who underwent pulmonary artery CT angiography from January 2015 to March 2022. Based on DECT imaging, 107 radiomic features were extracted for each patient using standardized image processing. After dividing the dataset into training and test sets, stepwise feature reduction based on reproducibility, variable importance and correlation analyses were performed to select the most relevant features; these were used to train and validate the gradient-boosted tree models.Receiver operating characteristics (ROC) analysis was utilized to evaluate the association between volumetric, laboratory data and adverse outcomes. In the central PE group, we observed a significant correlation between thrombus volumetrics and D-dimer levels (p=0.0037), as well as between thrombus volumetrics and hospitalization at the Intermediate Care Unit (IMC) (p=0.0001). In contrast, no statistically significant differences were identified in thrombus sizes between patients who experienced complications and those who had a favorable course (p=0.3162). The trained machine learning classifier achieved an accuracy of 61% and 55% in identifying patients with a complicated course, as indicated by an area under the ROC curve of 0.63 and 0.58. In conclusion, our findings indicate a positive correlation between D-dimer levels and central PE's pulmonary embolic burden. Thrombus volumetrics may serve as an indicator for complications and outcomes in acute PE patients. Thus, thrombus volumetrics, as opposed to D-dimers, could be an additional marker for evaluating embolic disease severity. Moreover, DECT-derived radiomic feature models show promise in identifying patients with a complicated course, such as hospitalization at IMC.

Frequency of atypical pulmonary manifestations of COVID-19 patients on chest CT-scan: a cross-sectional study

BackgroundChest CT is a crucial diagnostic tool in COVID-19 due to its high sensitivity. Typical chest CT findings such as reversed halo sign, vascular enlargement, and air-bronchogram are commonly recognized in the pandemic, but finding atypical signs is essential to diagnose. We aimed to investigate the prevalence of the atypical manifestations during the two months of 2022 and compare the results to studies conducted before its outbreak.MethodsThose patients with positive, positive RT-PCR tests were reevaluated to see if they met the inclusion and exclusion. Demographic information of patients, including age and sex, was recorded. The computed tomography (CT) examination was carried out using a 100-slice scanner (Philips Brilliance 6 CT Scanner). Two radiology specialists evaluated SARS-CoV-2 RT-PCR-positive patients for atypical pulmonary CT findings separately. The obtained data were evaluated using R software version 4.1.1.Results606 confirmed COVID-19 cases were included in this study based on inclusion and exclusion criteria during January and February 2022. 55% of patients were female, and the median age was 56 (IQR: 42, 69). More than half of the patients (59%) had atypical findings on their pulmonary CT examination. These findings showed that pleural abnormalities were the most frequent atypical finding. Among pleural abnormalities, pleural thickening was the most common (17%). The double halo sign represented the least frequent atypical sign (0.2%).ConclusionsAtypical findings were more prevalent in this study than its predecessors, while we acknowledge that other factors, such as study design and patient population, could have impacted it. The presence of atypical signs generally was not correlated with specific demographic groups, while some of these signs were more frequent in particular groups.

Deep learning to predict respiratory lung-tissue displacement from multi-phase pulmonary computed tomography images

Lung surgery, particularly lung puncture, is challenging because respiration motion significantly displaces lung tissues (e.g., blood vessels, trachea), including targeted lesions. To improve the success of lung surgery, it is essential to determine how lung tissues move during respiration. Early work used simple mathematical models (e.g., linear models) to predict lung-tissue displacement. However, the quality and quantity of pulmonary computed tomography (CT) images used to build such models were limited, preventing their universal application. Moreover, such mathematical models are too simple to describe how lung tissues are displaced during respiration. In this work, we analyzed the relationship between body-surface and lung-tissue displacement in a large number of multi-phase pulmonary CT images to create a model predicting lung-tissue displacement. First, a systematic method based on unsupervised learning approaches was designed to calculate the displacement of the body surface and lung tissues. Second, a deep neural network model was developed to predict lung-tissue displacement, using the body-surface displacement observed in CT imaging data as input. Multi-phase pulmonary CT images from 199 patients were collected to train and test our model. The validation experiment demonstrated that the prediction accuracy based on our deep learning model reached 79.8%, which outperformed traditional machine learning approaches like the linear model (79.2%), linear support vector regression model (79.2%), and support vector regression model with radial basis function (67.2%). These results demonstrated that our method could predict lung-tissue displacement based on body-surface displacement during respiration, potentially improving the success of lung puncture procedures.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329

Attention U-net for automated pulmonary fissure integrity analysis in lung computed tomography images

Computed Tomography (CT) imaging is routinely used for imaging of the lungs. Deep learning can effectively automate complex and laborious tasks in medical imaging. In this work, a deep learning technique is utilized to assess lobar fissure completeness (also known as fissure integrity) from pulmonary CT images. The human lungs are divided into five separate lobes, divided by the lobar fissures. Fissure integrity assessment is important to endobronchial valve treatment screening. Fissure integrity is known to be a biomarker of collateral ventilation between lobes impacting the efficacy of valves designed to block airflow to diseased lung regions. Fissure integrity is also likely to impact lobar sliding which has recently been shown to affect lung biomechanics. Further widescale study of fissure integrity’s impact on disease susceptibility and progression requires rapid, reproducible, and noninvasive fissure integrity assessment. In this paper we describe IntegrityNet, an attention U-Net based automatic fissure integrity analysis tool. IntegrityNet is able to predict fissure integrity with an accuracy of 95.8%, 96.1%, and 89.8% for left oblique, right oblique, and right horizontal fissures, compared to manual analysis on a dataset of 82 subjects. We also show that our method is robust to COPD severity and reproducible across subject scans acquired at different time points.

Computed tomography findings in patients with pulmonary tuberculosis and diabetes at an infectious disease hospital in China: a retrospective cross-sectional study

BackgroundThis study aimed to investigate the relationship between active pulmonary tuberculosis (TB) and type 2 diabetes mellitus (T2DM) by analysing the clinical features and computed tomography (CT) findings of patients with active pulmonary TB and comorbid T2DM (TB-DM) in the LiangShan Yi regions.MethodsWe collected data from 154 hospitalised patients with TB-DM initially confirmed at an infectious disease hospital in the Liangshan Yi Autonomous Prefecture between 1 and 2019, and 31 December 2021. These were matched by sex and age ± 3 years to 145 hospitalised patients with initially confirmed pulmonary TB without comorbid T2DM (TB-NDM) over the same period. The clinical characteristics of the two groups were analysed separately. Three group-blinded radiologists independently analysed the CT findings and classified them into mild-to-moderate and severe groups. Severe chest CT lesion refers to a lesion that is less diffused or moderately dense and either exceeds the total volume of one lung, a high-density fused lesion greater than one-third of the volume of one lung, or a cavitary lesion with a maximum diameter ≥ 4 cm.ResultsNo significant differences were observed in the presentation of clinical features. Regarding the severity of chest CT manifestation, patients with TB-DM had significantly more severe TB than those with TB-NDM (89.61% vs. 68.97%, P < 0.0001). Regarding CT findings, patients with TB-DM had higher proportions of consolidation (79.22% vs. 52.41%, P < 0.0001), cavitary lesions (85.06% vs. 59.31%, P < 0.0001), bronchiectasis (71.43% vs. 31.03%, P < 0.0001), exudative lesions (88.96% vs. 68.28%, P < 0.0001), and fibrous lesions (93.51% vs. 68.97%, P < 0.0001) than patients with TB-NDM. In conclusion, patients with TB-DM have more severe pulmonary TB CT findings than those without. There were no significant differences in the distribution of lesions in the lung lobes between TB-DM and TB-NDM patients.ConclusionsAmong patients hospitalised with pulmonary TB, those with T2DM had more severe findings on chest CT than those without T2DM. However, the clinical presentation was not significantly different.

The Great Contrast Shortage of 2022-Lessons learnt in Australia.

Computed tomography (CT) imaging is one of the most commonly used diagnostic tools. Iodine-based contrast media (IBCM) are frequently administered intravenously to improve soft tissue contrast in a wide range of CT scans. Supply chain disruptions triggered by the SARS-CoV-19 pandemic led to a global shortage of IBCM in mid-2022. The purpose of this study was to explore the impact of this shortage on the delivery of healthcare in Western Australia. We performed a single-centre retrospective analysis of the provision of CT studies, comparing historical patterns to the shortage period. We focussed our attention on the total number of CT scans (noncontrast CT [NCCT] and contrast-enhanced CT [CECT]) and also specifically CT pulmonary angiogram (CTPA) and CT neck angiogram with or without inclusion of circle of Willis (CTNA) examinations. We also examined whether a decrease was compensated by increasing frequency of alternate examinations such as ventilation/perfusion (V/Q) scans, carotid Doppler ultrasound studies and Magnetic Resonance Angiograms (MRAs). Since 2012, there has been an approximate linear increase in the frequency of CT examinations. During the period of contrast shortage, there was an abrupt drop-off by approximately 50% in the CECT, CTPA and CTNA groups compared with the preceding 6 weeks (49%, 55% and 44%, respectively, with P < 0.001 in all cases). During the contrast shortage, the frequency of V/Q scans increased fivefold (from 13 to 65; P < 0.001). However, the provision of carotid Doppler ultrasound studies and MRAs remained approximately stable in frequency across recent time intervals. Our findings demonstrate that the IBCM shortage crisis had a very significant impact on the delivery of healthcare. While V/Q scans could (partially) substitute for CTPA studies in suspected pulmonary emboli, there appeared to be no valid alternative for CTNA studies in stroke calls. The unexpected and critical shortage of IBCM forced healthcare professionals to conserve resources, prioritise indications, triage patients based on risk, explore alternate imaging strategies and prepare for similar events recurring in the future.

Analysis of pulmonary artery variation based on 3D reconstruction of CT angiography.

Objective: The aim of this study is to acquire pulmonary CT (Computed tomography) angiographic data for the purpose of creating a three-dimensional reconstruction. Additionally, we aim to analyze the features and deviations of the branches in both pulmonary lobes. This information is intended to serve as a more comprehensive and detailed reference for medical professionals when conducting preoperative evaluations and devising surgical plans. Method: Between August 2019 and December 2021, 420 patients were selected from the thoracic surgery department at the First Hospital of Jilin University, and underwent pulmonary 64 channel contrast enhanced CT examinations (Philips ICT 256). The images were acquired at a 1.5mm slice thickness, and the DCM files that complied with DICOM (Digital Imaging and Communications in Medicine) standards were analysed for 3D (three dimensional) reconstruction using Mimics 22.0 software. The reconstructed pulmonary artery models were assessed by attending chest surgeons and radiologists with over 10years of clinical experience. The two-dimensional image planes, as well as the coronary and sagittal planes, were utilized to evaluate the arteries. The study analyzed the characteristics and variations of the branches and courses of pulmonary arteries in each lobe of the lungs, with the exception of the subsegmental arterial system. Two chest surgeons and two radiologists with professional titles-all of whom had over a decade of clinical experience-jointly evaluated the 3D models of the pulmonary artery and similarly assessed the characteristics and variations of the branches and courses in each lobe of the lungs. Results: Significant variations were observed in the left superior pulmonary artery across the 420 subjects studied. In the left upper lobe, the blood supply of 4 arteries accounted for 50.5% (n = 212), while the blood supply of 2 arteries in the left lower lobe was the most common, accounting for 79.5% (n = 334). The greatest variation in the right pulmonary artery was observed in the branch supply of the right upper lobe mediastinal artery. In the majority of cases (77.9%), there were two arteries present, which was the most common configuration observed accounting for 64% (n = 269). In the right inferior lobe of the lung, there were typically 2-4 arteries, with 2 arteries being the most common configuration (observed in 79% of cases, n = 332). Conclusion: The three-dimensional reconstruction of pulmonary artery CT angiography enables clear observation of the branches and distribution of the pulmonary artery while also highlighting any variations. This technique holds significant clinical value for preoperative assessments regarding lesions and blood vessels.

CT-defined pectoralis muscle mass and muscle density are associated with mortality in acute pulmonary embolism. A multicenter analysis

Computed tomography (CT) defined muscle mass can be used as a surrogate parameter for sarcopenia. The present study used thoracic CT to assess pectoralis muscle area and density as an imaging biomarker for prognosis of 30-day mortality in patients with acute pulmonary embolism (PE) METHODS: The clinical database was retrospectively screened for patients with thoracic CT in 3 centers. Pectoralis musculature was measured on axial slices of the thoracic CT at the level of T4 of contrast enhanced pulmonary angiography CT. Skeletal muscle area (SMA), skeletal muscle index (SMI), muscle density and gauge were calculated. Overall, 981 patients (440 female, 44.9%) with a mean age of 63.5±15.9 years were included into the study and 144 patients (14.6%) died within the 30-days period. Every pectoral muscle value was higher in survivors compared to non-survivors (exemplarily for SMI 9.9±3.5cm2/m2 versus 7.8±2.6cm2/m2, p<0.001). Moreover, 91 patients were defined as hemodynamically instable (9.3%). Comparable, every pectoral muscle parameter was higher in patients with hemodynamically stable course compared to instable course. Different muscle variables are related to 30-day mortality: SMA, OR=0.94 (95%CI= (0.92; 0.96), p<0.001); SMI, OR=0.78 (95%CI= (0.72; 0.84), p<0.001); muscle density, OR=0.96 (95%CI= (0.94; 0.97), p<0.001); muscle gauge OR=0.96 (95%CI= (0.94; 0.99), p<0.001). SMI and muscle density were independently associated with 30-days mortality: SMI, OR=0.81 (95%CI= (0.75; 0.88), p<0.001); muscle density: OR=0.96 (95%CI= (0.95; 0.98), p<0.001). Parameters of the pectoralis musculature are associated with 30-day mortality in patients with acute PE. These findings should lead to an independent validation study and ultimately to the inclusion into clinical routine as a prognostic factor.

Pulmoner Emboli ile D-dimer Düzeyinin Korelasyonu ve Yaşa Göre Eşik Değerlerinin Belirlenmesi

Aim: This study was conducted to determine the D-dimer threshold levels according to age in patients diagnosed with pulmonary embolism (PE) by pulmonary computed tomography (CT) angiography. Material and Methods: Patients who had pulmonary CT angiography performed and whose D-dimer test was obtained with the suspicion of pulmonary embolism (PE) in the emergency department of a tertiary university hospital between January 01, 2015, and December 30, 2019, were included in the study. The demographic characteristics of the patients, imaging reports, and 1-month mortality were evaluated retrospectively by examining the hospital information management system and patient files. Patients with missing records and patients who had diagnostic examinations other than the preliminary diagnosis of PE were excluded from the study. Results: Among the 6240 patients included in the study, 58.1% were female. The mean age of the patients was 43±17 years. Of the 1,507 patients who had a pulmonary CT angiogram, 9.8% were diagnosed with PE. When we looked at the 1-month mortality of 6240 patients, whose D-dimer assay was analyzed with the preliminary diagnosis of PE, we found that 0.3% of patients died. The monthly mortality rate was 0.7% in patients with high D-dimer levels while the mortality rate in patients with normal D-dimer levels was 0.1%. A positive and statistically significant correlation was found between the location of the pulmonary embolism in pulmonary CT angiography and the D-dimer level (spearman’s rho= 0.251, p&amp;lt;0.001). The cut-off D-dimer value in predicting the presence of PE in all age groups was calculated as 1.34 mg/L. The cut-off values of D-dimer to predict the presence of PE according to age ranges was calculated as 1.18 mg/L (&amp;lt;50 years), 1.19 mg/L (50-60 years), 1.58 mg/L (60-70 years) 1.79 mg/L (70-80 years), 2.83 mg/L (&amp;gt;80 years) respectively. Conclusion: As age increases, the D-dimer cut-off value for the diagnosis of PE also increases. There is a positive and significant relationship between D-dimer level and pulmonary embolism severity.

Clinical value of deep vein thrombosis density on pre-contrast and post-contrast lower-extremity CT for prediction of pulmonary thromboembolism.

There was a lack of studies assessing the relationship between deep vein thrombosis (DVT) Hounsfield unit (HU) density and pulmonary thromboembolism (PTE). To evaluate the clinical value of DVT density measured on pre- and post-contrast lower-extremity computed tomography (CT) for the prediction of PTE. From 2017 to 2021, patients who underwent pulmonary CT angiography within one week after diagnosis of DVT on lower-extremity CT were included in this retrospective study. Then, the patients without PTE were included in "DVT group" and those with both DVT and PTE were included in the "DVT-PTE group." The DVT HU density was measured by drawing free-hand region of interests (ROIs) within the thrombus at the most proximal filling defect level. A receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of DVT density for the risk of PTE. This study included a total of 94 patients (DVT group: n=56; DVT-PTE group: m=38). DVT density was significantly higher in the DVT-PTE group than the DVT group in both pre-contrast (53.5 ± 6.2 HU vs. 44.1 ± 7.9 HU; P < 0.001) and post-contrast CT (67.0 ± 8.6 HU vs. 57.1 ± 10.6 HU; P < 0.001). ROC analysis revealed that the area under curve, sensitivity, and specificity for predicting the risk of PTE were 0.739, 71.1%, and 64.2%, respectively, at a DVT density cutoff of 48.2 HU on pre-contrast CT and were 0.779, 73.7%, and 69.6% at a DVT density cutoff of 61.8 HU on post-contrast CT. The DVT density on both pre- and post-contrast CT could be a predictive factor of PTE.

CT imaging in idiopathic out-of-hospital cardiac arrest: An assessment of current practice and diagnostic utility

Idiopathic Out-Of-Hospital Cardiac Arrest (OHCA) requires urgent treatment. Early Computed Tomography (CT) imaging may be useful to aid diagnosis. We aimed to determine current CT imaging practice, safety, and diagnostic value in this patient population. This study was a single-centre, retrospective cohort study of patients presenting to the Emergency Department (ED) of the Royal Infirmary of Edinburgh with idiopathic non-traumatic OHCA and Return Of Spontaneous Circulation (ROSC). Between 1st January 2016 and 31st December 2019, 140 of 156 (90%) eligible patients underwent 195 CT scans identifying the cause of OHCA in 6 (4%). CT head diagnosed one ischaemic and three haemorrhagic strokes, and CT pulmonary angiogram diagnosed one acute coronary syndrome and one pulmonary embolism. CT head (134), CT pulmonary angiogram (25) and CT cervical spine (16) were the commonest scans. 68 of 195 (35%) CT scans showed important pathology, mostly secondary to OHCA. CT imaging was safe with no cases of contrast nephropathy, allergic reaction, or other complications. The diagnostic value of CT imaging in this patient population was limited. However, imaging was a valuable method of identifying other important secondary pathology.

New grading system for post-COVID-19 pulmonary fibrosis based on computed tomography findings.

Background:There is currently no objective computed tomography (CT)-defined grading system for coronavirus disease (COVID-19)-related pulmonary fibrosis. We propose a CT-based radiological scale that adapts the histological fibrosis scale to pulmonary fibrosis CT findings, to evaluate possible predictive factors for the degree of fibrosis in these patients.Methods:A new radiological fibrosis grading system was created based on existing histological fibrosis scales. One hundred forty-seven COVID-19 patients with any degree of fibrosis on CT were evaluated. Smoking status, the presence of hypertension, the duration of hospital stays, the presence of comorbid diseases, and the levels of prognostic and predictive factors for COVID-19 were evaluated, and how these parameters affected the fibrosis scores was examined.Results:Of 147 patients, 17.7% had grade 1, 17% had grade 2, 51.7% had grade 3, and 13.6% had grade 4 fibrosis. ANOVA revealed statistically significant relationships between the fibrosis scores and lactate dehydrogenase values, lymphocyte count, C-reactive protein level, and length of hospital stay. Smoking, advanced age, hypertension, and male sex showed significantly higher scores for fibrosis.Conclusions:Using our CT-defined lung fibrosis grading system, we could predict the severity of fibrosis as well as the resultant lung pathology in COVID-19 patients. Thus, disease exacerbation and development of permanent severe fibrosis can be prevented using the appropriate treatment methods in high-risk patients.

Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma.

Objective Spread through air space (STAS) is an invasive characterization of lung adenocarcinoma and is regarded as a risk factor for poor prognosis. The aim of this study is to develop a random forest model for preoperative prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma. Methods 92 patients with stage IA lung adenocarcinoma, who underwent computed tomography (CT) scan and surgical resection, were retrospectively reviewed. Each pulmonary nodule was automatically segmented by artificial intelligence (AI) software, and its CT-based radiomics were extracted. All patients were pathologically classified into STAS-negative and STAS-positive cohorts; then, clinical pathological and CT-based radiomics were compared between the two cohorts. Finally, a prediction model for evaluating STAS status in stage IA lung adenocarcinoma was established by a random forest model. Results Among 92 patients with stage IA lung adenocarcinoma, STAS positive was identified in 19 patients. The random forest classification model identified predictive features, including CT maximum value, consolidation to tumor ratio (CTR), 3D diameter, CT mean value, entropy, and CT minimum value. The misclassification rate of the random forest model is only 7.69%. Conclusion The risk factors of STAS in stage IA lung adenocarcinoma can be effectively identified based on the random forest model, and the hierarchical management of characteristic risk can be effectively realized. A random forest model for predicting STAS in IA lung adenocarcinoma is simple and practical.