Value Of Chest Computed Tomography Research Articles

Opportunistic Assessment of Hip Fracture Risk Based on Chest CT.

Hip fracture (HF) has been described as the "last fracture of life" in the elderly, so the assessment of HF risk is extremely important. Currently, few studies have examined the relationship between imaging data from chest computed tomography (CT) and HF. This study demonstrated that pectoral muscle index (PMI) and vertebral body attenuation values could predict HF, aiming to opportunistically assess the risk of HF in patients without bone mineral density (BMD) based on chest CT for other diseases. In the retrospective study, 800 participants who had both BMD and chest CT were enrolled from January 2021 to January 2024. After exclusion, 472 patients were finally enrolled, divided into the healthy control (HC) group and the HF group. Clinical data were collected, and differences between the two groups were compared. A predictive model was constructed based on the PMI and CT value of the fourth thoracic vertebra (T4HU) by logistic regression analysis, and the predictive effect of the model was analyzed by using the receiver operating characteristic (ROC) curve. Finally, the clinical utility of the model was analyzed using decision curve analysis (DCA) and clinical impact curves. Both PMI and T4HU were lower in the HF group than in the HC group (p < 0.05); low PMI and low T4HU were risk factors for HF. The predictive model incorporating PMI and T4HU on the basis of age and BMI had excellent diagnostic efficacy with an area under the curve (AUC) of 0.865 (95% confidence interval [CI]: 0.830-0.894, p < 0.01), sensitivity and specificity of 0.820 and 0.754, respectively. The clinical utility of the model was validated using calibration curves and DCA. The AUC of the predictive model incorporating BMD based on age and BMI was 0.865 (95% CI: 0.831-0.895, p < 0.01), with sensitivity and specificity of 0.698 and 0.711, respectively. There was no significant difference in diagnostic efficacy between the two models (p = 0.967). PMI and T4HU are predictors of HF in patients. In the absence of dual-energy x-ray absorptiometry (DXA), the risk of HF can be assessed by measuring the PMI and T4HU on chest CT examination due to other diseases, and further treatment can be provided in time to reduce the incidence of HF.

Effect of Model-Based Iterative Reconstruction on Image Quality of Chest Computed Tomography for COVID-19 Pneumonia.

This study aimed to compare the image quality of chest computed tomography (CT) scans for COVID-19 pneumonia using forward-projected model-based iterative reconstruction solution-LUNG (FIRST-LUNG) with filtered back projection (FBP) and hybrid iterative reconstruction (HIR). The CT images of 44 inpatients diagnosed with COVID-19 pneumonia between December 2022 and June 2023 were retrospectively analyzed. The CT images were reconstructed using FBP, HIR, and FIRST-LUNG-MILD/STANDARD/STRONG. The CT values and noise of the lumen of the main trachea and erector spine muscle were measured for each group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective evaluations included overall image quality, noise, streak artifact, visualization of normal lung structures, and abnormal CT features. One-way analysis of variance was used to compare the objective and subjective indicators among the five groups. The task-based transfer function was derived for three distinct contrasts representing anatomical structures, lower-contrast lesion, and higher-contrast lesion. The results of the study demonstrated significant differences in image noise, SNR, and CNR among the five groups (P < 0.001). The FBP images exhibited the highest levels of noise and the lowest SNR and CNR among the five groups (P < 0.001). When compared to the FBP and HIR groups, the noise was lower in the FIRST-LUNG-MILD/STANDARD/STRONG group, while the SNR and CNR were higher (P < 0.001). The subjective overall image quality score of FIRST-LUNG-MILD/STANDARD was significantly better than FBP and FIRST-LUNG-STRONG (P < 0.001). FIRST-LUNG-MILD was superior to FBP, HIR, FIRST-LUNG-STANDARD, and FIRST-LUNG-STRONG in visualizing proximal and peripheral bronchovascular and subpleural vessels (P < 0.05). Additionally, FIRST-LUNG-MILD achieved the best scores in evaluating abnormal lung structure (P < 0.001). The overall interobserver agreement was substantial (intraclass correlation coefficient = 0.891). The task-based transfer function 50% values of FIRST reconstructions are consistently higher compared to FBP and HIR. The FIRST-LUNG-MILD/STANDARD algorithm can enhance the image quality of chest CT in patients with COVID-19 pneumonia, while preserving important details of the lesions, better than the FBP and HIR algorithms. After evaluating various COVID-19 pneumonia lesions and considering the improvement in image quality, we recommend using the FIRST-LUNG-MILD reconstruction for diagnosing COVID-19 pneumonia.

Diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on duration of symptoms.

Chest computed tomography (CT) imaging provides results more rapidly and with higher sensitivity than reverse transcription polymerase chain reaction in diagnosis of COVID-19. To evaluate diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on age and duration of symptoms. A retrospective study conducted during December 2020 to June 2021 in a tertiary care hospital, India. Total 495 patients with typical clinical symptoms of COVID-19, reverse transcription polymerase chain reaction positive for COVID-19 and had undergone chest CT imaging were included. Descriptive statistical analysis was performed for all the variables. Receiver operating characteristic curve analysis was used to determine threshold value of chest CT severity score (CT_SS) based on duration of symptoms and age to diagnose COVID-19. Mean age of patients was 61.86 ± 10.77 years and 367 (71.4%) patients were male. Ground glass opacities were observed in 456 (92.1%) patients and in 332 (67.1%) patients, multilobes were affected. Total CT_SS showed positive correlation with age (r = 0.257) and duration of symptoms (r = 0.625). Total CT_SS >6 after a duration of 2 days of symptoms identified COVID-19 cases with sensitivity 90.8% (95% confidence interval [CI]: 87.5%-93.5%) and specificity 84.6% (95% CI: 76.2%-90.9%). Total CT_SS >11 in patients aged more than 60 years identified COVID-19 cases with sensitivity 47.4% (95% CI: 41.2%-53.6%) and specificity 87.3% (95% CI: 82.3%-91.4%). Threshold value of CT_SS determined will help to expedite diagnosis of COVID-19 patients by the clinicians in an early stage especially in India and other developing countries which have a high patient volume and limited health resources.

The value of non-contrast chest CT in the prediction of myocardial injury in patients with the COVID-19 Omicron variant

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant associated myocardial injury seriously affected the patient's health. Chest computed tomography (CT) is an essential imaging diagnostic tool for evaluating lung diseases in these patients, but its value in the diagnosis of myocardial injury remains unknown. The purpose of this study was to evaluate the lung lesions in patients with Omicron infection with or without myocardial injury, and to evaluate the predictive value of non-contrast chest CT in such patients with myocardial injury. We enrolled 122 consecutive hospitalized patients with laboratory-confirmed coronavirus disease 2019 (COVID-19) for non-contrast chest CT examination. These patients were divided into two groups according to whether myocardial injury occurred. Myocardial injury was defined as a Troponin I level above the 99th-percentile upper reference limit (0.04 ng/mL). The imaging manifestations of the patients’ lungs were evaluated. Myocardial CT value, left atrium (LA) size, long diameter of left ventricular (LV), and cardiothoracic ratio (CTR) were recorded. Multivariate logistic analysis was performed to identify the predictive factors associated with myocardial injury. Of 122 patients, 61 patients (50%) had myocardial injury. Compared with patients without myocardial injury, there was worse NYHA class, more critical patients, higher incidence of bronchial meteorology, larger area and percentage of lung lesions, diameters of LA, and lower myocardial CT value in the myocardial injury group (P < 0.05). Troponin I concentration in patients with myocardial injury group showed negative correlation with myocardial CT value (r = − 0.319, P = 0.012). The multivariable logistic regression analysis showed that disease severity status (OR 2.279; 95% CI 1.247–4.165, P = 0.007), myocardial CT value (OR 0.849; 95% CI 0.752–0.958, P = 0.008), neutrophil count (OR 1.330; 95% CI 1.114–1.587, P = 0.002) were independent predictors of myocardial injury. The discrimination of the model was good (C-statistic = 0.845, 95% CI 0.775–0.914) and well calibrated with a Hosmer–Lemeshow test for goodness of fit (P = 0.476). Patients infected with Omicron with myocardial injury had more severe lung disease than those without myocardial injury. Non-contrast chest CT can be a useful method of detection of myocardial injury in Omicron infection patients.

Could initial CT chest manifestation in patients hospitalized with COVID 19 pneumonia predict outcome on short term basis.

Chest computed tomography (CT) can be used to monitor the course of the disease or response to therapy. Therefore, our study was designed to identify chest CT manifestations that can predict the outcome of patients on short term follow-up. This was a retrospective study wherein we reviewed chest CT scans of 112 real-time reverse transcription polymerase chain reaction positive patients admitted to our hospital. All 112 patients underwent follow-up chest CT at a time interval of 4 to 42 days. Our study included 83 male and 29 female who were positive for COVID 19 infection and admitted to the hospital with positive chest CT findings. All patients underwent follow-up chest CT, and the outcomes were categorized as resolution, regression, residual fibrosis, progression, or death. These proportions were 5.4%, 48.2%, 24.1%, 14.3%, and 8%, respectively. The only significant factor in determining the complete resolution of chest CT was oligo-segmental affection (P = .0001). The main CT feature that significantly affected the regression of chest CT manifestations was diffuse nodular shadows (P = .039). The CT features noted in patients with residual fibrosis were interstitial thickening, with a P value of .017. The mono-segmental process significantly affected progression (P = .044). The significant factors for fatality were diffuse crazy paving, pleural effusion, and extra-thoracic complications (P = .033, .029, and .007, respectively). The prognostic value of the first admission CT can help assess disease outcomes in the earliest phases of onset. This can improve resource distribution.

Diagnosing Lung Abnormalities Related to Heart Failure in Chest Radiogram, Lung Ultrasound and Thoracic Computed Tomography

Heart failure (HF) is a multidisciplinary disease affecting almost 1-2% of the adult population worldwide. Symptoms most frequently reported by patients suffering from HF include dyspnoea, cough or exercise intolerance, which is equally often observed in many pulmonary diseases. The spectrum of lung changes related to HF is wide. The knowledge of different types of these abnormalities is essential to distinguish patients with HF from patients with lung diseases or both disorders and thus avoid unnecessary diagnostics or therapies. In this review, we aimed to summarise recent research concerning the spectrum of lung abnormalities related to HF in three frequently used lung imaging techniques: chest X-ray (CXR), lung ultrasound (LUS) and chest computed tomography (CT). We discussed the most prevalent abnormalities in the above-mentioned investigations in the context of consecutive pathophysiological stages identified in HF: (i) redistribution, (ii) interstitial oedema, and (iii) alveolar oedema. Finally, we compared the utility of these imaging tools in the clinical setting. In conclusion, we consider LUS the most useful and promising imaging technique due to its high sensitivity, repeatability and accessibility. However, the value of CXR and chest CT is their potential for establishing a differential diagnosis.

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans

BackgroundThe coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. MethodsA deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). ResultsLinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. ConclusionsThe proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

Contrast-Enhanced Chest Computed Tomography (CT) Scan with Low Radiation and Total Iodine Dose for Lung Cancer Detection Using Adaptive Statistical Iterative Reconstruction

Background: Contrast-enhanced chest computed tomography (CT) is useful for the detection and follow-up of patients with lung cancer. However, reaching balance between diagnostic image quality, radiation dose, and iodixanol dose is a cause of concern. Objectives: To investigate the clinical value of adaptive statistical iterative reconstruction (ASIR) in reducing the iodixanol content and radiation dose during contrast-enhanced chest CT scan for patients diagnosed with lung masses/nodules based on the analysis of image quality. Patients and Methods: This prospective study was conducted on 80 patients diagnosed with nodules or masses, who required contrast-enhanced chest CT scans. The experimental group (n = 40) was subjected to iohexol at a high concentration (350 mgI/L) with a tube voltage of 120 kVp and a filter back projection (FBP) reconstruction algorithm. The comparison group (n = 40) was subject to iodixanol at a lower concentration (270 mgI/L) with a tube voltage of 100 kVp and ASIR (blending ratio, 40%). The radiation dose and total iodixanol content, as well as subjective and objective evaluations of image quality, were analyzed and compared. Results: The two groups obtained non-significantly different subjective scores for five structures detected in the lung window and five structures detected in the mediastinal window, as well as the overall image (P &gt; 0.05 for all). Both the two-group images obtained diagnosis-acceptable scores (≥ 3 points) on displays of 10 structures and overall image quality. The mean CT value of vessels (100 kVp vs. 120 kVp: 314.90 ± 23.42 vs. 308.93 ± 21.40; P &gt; 0.05), standard deviation (13.03 ± 0.88 vs. 12.83 ± 0.90; P &gt; 0.05), and contrast-to-noise ratio (20.77 ± 2.20 vs. 20.36 ± 1.94; P &gt; 0.05) were not significantly different between two groups. However, the CT dose index, dose-length product, effective dose, and total iodine dose were reduced by 27.58%, 36.65%, 36.59%, and 22.86% in the 100-kVp group compared to the 120-kVp group. Conclusion: The ASIR showed great potential in reducing the radiation dose and iodine contrast dose, while maintaining good image quality and providing strong confidence for the diagnosis of lung cancer.

Comparison of Two-dimensional and Three-dimensional Features of Chest CT in the Diagnosis of Invasion of Pulmonary Ground Glass Nodules

At present, more and more studies predict invasive adenocarcinoma (IAC) through three-dimensional features of pulmonary nodules, but few studies have confirmed that three-dimensional features have more advantages in diagnosing IAC than traditional two-dimensional features of pulmonary nodules. This study analyzed the differences of chest computed tomography (CT) features between IAC and minimally invasive adenocarcinoma (MIA) from three-dimensional and two-dimensional levels, and compared the ability of diagnosing IAC. The non-invasive adenocarcinoma group includes precursor glandular lesions (PGL) and minimally invasive adenocarcinoma (MIA). The clinical data of 1,045 patients with ground glass opacity (GGO) from January to December 2019 were collected. Then the correlation between preoperative CT image characteristics and pathological results were analyzed retrospectively. The independent influencing factors for the identification of IAC were screened out according to two-dimensional and three-dimensional classification by multivariate Logistic regression and the cut-off point for the identification of IAC was found out through the receiver operating characteristic (ROC) curve. At last, the ability of diagnosing IAC was evaluated by Yoden index. The diameter of nodule, the diameter of solid component, the diameter of mediastinal window nodule in two-dimensional factors, and the volume of nodule, the volume of solid part and the average CT value in three-dimensional factors were independent risk factors for the diagnosis of IAC. These factors were arranged by Yoden index: solid partial volume (0.601)>nodule volume (0.536)>solid component diameter (0.525)>nodule diameter (0.518)>mediastinal window nodule diameter (0.488)>proportion of solid component volume (0.471)>1-tumor disappearance ratio (TDR) (0.468)>consolidation tumor ratio (CTR) (0.394)>average CT value (0.380). The CT features of three-dimensional are better than two-dimensional in the diagnosis of IAC, and the size of solid components is better than the overall size of nodules.

Comparison of Chest Computed Tomography Differential Diagnosis of Ground Glass Opacities in the COVID-19 Patients

Aim: To compare chest computed tomography (CT) diagnosis of ground glass opacities in the COVID-19 patients. Study design: Retrospective study. Place and duration of study: Department of Radiology, Ghulam Muhammad Mahar Medical College Sukkur from 1st July 2020 to 31 August 2021. Methodology: Fifty patients on differential CT diagnosis of ground glass opacities seen in COVID 19 patients were enrolled. Thoracic CT images by applying auto exposure-control settings and ranges of scan were done. The noise-index was kept as 12.3. Using helical 16 slice Alexion CT-Toshiba. Keeping a comparison with viral infection CT images a list of 7 signs which were positive for Covid CT scan were recorded. Peripheral lesions meant any lesions which effects peripheral area up to 3 to 4 cm lung periphery with/without having central dispersal. A hazy-opacity was termed as ground glass. Results: Mean age of the patients was 49.1±10.2 years with 27 (54%) males and 23(46%) females. Mix ground glass opacitites and consolidation were also the features of the CT imaging in coronavirus posisitve cases. Man-Whitney test results showed that combined-CT scoring had a SE value as 0.044 with a confidence interval between 0.756-0.927. Comparing the differential CT values within COVID and non COVID patients based on RT PCR results it was observed that posterior region lower lobe involvement was a feature of COVID-19 patients while crazy paving pattern and peripheral distribution was also seen in corona patients. Conclusion: Present study highlights that chest CT helps in differentitaing corona virus from other causes of pneumonias and grond glass opacities. Key words: Chest CT; Differential diagnosis; COVID-19; Ground glass opacities (GGO)

The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study

To investigate the effect of a new deep learning image reconstruction (DLIR) algorithm on the detection, characterization and image quality of pulmonary nodules (PNs) in ultra-low dose chest computed tomography (CT) in comparison with the adaptive statistical iterative reconstruction (ASIR-V) algorithm. Nine artificial pulmonary nodules [six ground glass nodules (GGNs) and three solid nodules (SNs); density: -800 HU, -630 HU, 100 HU; diameter: 12 mm, 10 mm, 8 mm] were randomly placed in a thorax anthropomorphic phantom (Lungman, Kyoto Kagaku Inc.) and scanned on a 256-row CT (Revolution CT, GE Healthcare). Eight scans were performed at 70 kVp with different tube currents (20, 30, 50, 70, 90, 100, 120, 150 mA). Raw data were reconstructed using the filtered back projection (FBP), ASIR-V (30%, 50%, 80%) and DLIR (Low, Medium, High; TrueFidelity™) at 0.625 mm thickness. The effective radiation dose was recorded. All images were automatically analyzed using a commercially available artificial intelligence software (Intelligent 4D Imaging System for Chest CT 5.5, YITU Healthcare) and CT value, standard deviation (SD), long and short diameters of each nodule and SD of air (background) were measured. The detection rate, deformation degree (long diameter/short diameter), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary nodules were calculated. Nodule CT values were the same in all mA settings for all three types of reconstruction algorithms (all P>0.05). DLIR groups had significantly lower SD and higher SNR and CNR values, with better overall image quality than ASIR-V and FBP groups at each mA, ranging from 65-85% reduction in SD, 67-83% increase in SNR with DLIR-H over 50%ASIR-V and 75-91% reduction in SD and 77-89% increase in SNR with DLIR-H over FBP (all P<0.05). At ultra-low dose conditions (30 mA), the DLIR-H images had the highest detection rate of PNs (100%). In addition, the DLIR-M had a minimal negative effect on the characterization of PNs. DLIR algorithm can be a potential reconstruction technique to optimize image quality and improve detection rate of PNs in ultra-low dose lung screening.

Biomarker evaluation for prognostic stratification of patients with COVID-19: the added value of quantitative chest CT.

Aim: Pulmonary disease burden and biomarkers are possible predictors of outcomes in patients with COVID-19 and provide complementary information. In this study, the prognostic value of adding quantitative chest computed tomography (CT) to a multiple biomarker approach was evaluated among 148 hospitalized patients with confirmed COVID-19. Materials & methods: Patients admitted between March and July 2020 who were submitted to chest CT and biomarker measurement (troponin I, D-dimer and C-reactive protein) were retrospectively analyzed. Biomarker and tomographic data were compared and associated with death and intensive care unit admission. Results: The number of elevated biomarkers was significantly associated with greater opacification percentages, lower lung volumes and higher death and intensive care unit admission rates. Total lung volume <3.0 l provided further stratification for mortality when combined with biomarker evaluation. Conclusion: Adding automated CT data to a multiple biomarker approach may provide a simple strategy for enhancing risk stratification of patients with COVID-19.

Kernel Conversion for Robust Quantitative Measurements of Archived Chest Computed Tomography Using Deep Learning-Based Image-to-Image Translation.

Chest computed tomography (CT) is used to screen for lung cancer and evaluate pulmonary and extra-pulmonary abnormalities such as emphysema and coronary artery calcification, particularly in smokers. In real-world practice, lung abnormalities are visually assessed using high-contrast thin-slice images which are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. In contrast, accurate CT quantification requires low-contrast thin-slice images with low noise, which are generated using soft reconstruction kernels. However, only sharp-kernel thin-slice images are archived in many medical facilities due to limited data storage space. This study aimed to establish deep neural network (DNN) models to convert sharp-kernel images to soft-kernel-like images with a final goal to reuse historical chest CT images for robust quantitative measurements, particularly in completed previous longitudinal studies. By using pairs of sharp-kernel (input) and soft-kernel (ground-truth) images from 30 patients with chronic obstructive pulmonary disease (COPD), DNN models were trained. Then, the accuracy of kernel conversion based on the established DNN models was evaluated using CT from independent 30 smokers with and without COPD. Consequently, differences in CT values between new images converted from sharp-kernel images using the established DNN models and ground-truth soft-kernel images were comparable with the inter-scans variability derived from repeated phantom scans (6 times), showing that the conversion error was the same level as the measurement error of the CT device. Moreover, the Dice coefficients to quantify the similarity between low attenuation voxels on given images and the ground-truth soft-kernel images were significantly higher on the DNN-converted images than the Gaussian-filtered, median-filtered, and sharp-kernel images (p < 0.001). There were good agreements in quantitative measurements of emphysema, intramuscular adipose tissue, and coronary artery calcification between the converted and the ground-truth soft-kernel images. These findings demonstrate the validity of the new DNN model for kernel conversion and the clinical applicability of soft-kernel-like images converted from archived sharp-kernel images in previous clinical studies. The presented method to evaluate the validity of the established DNN model using repeated scans of phantom could be applied to various deep learning-based image conversions for robust quantitative evaluation.

Value of chest CT over skeletal surveys in detection of rib fractures in pediatric patients

Recent literature has raised concerns about the sensitivity and accuracy of radiographs at diagnosing rib fractures. Studies have shown that chest computed tomography (CT) has far greater sensitivity at detecting rib fractures than radiographs. The purpose of this study was to evaluate the sensitivity of skeletal survey (SS) radiographs at diagnosis of rib fractures compared to CT in the pediatric population. This retrospective review included 57 patients who had undergone both a SS and a CT chest or CT chest/abdomen/pelvis within 30 days of each other for the indication of either non-accidental (NAT) or accidental trauma between 2009 and 2017. Images and reports were analyzed by a pediatric radiology fellow for presence/absence of fracture, evidence of healing and location of rib fracture, including rib level, location within the rib (anterior, lateral, and posterior), and side (right versus left). 225 rib fractures were identified in 25 patients on CT. 38 of those fractures were missed on the preceding SS, yielding a miss rate of 17%. Acute fractures were more likely to be missed than chronic or healing fractures (p ≤ 0.01). Location within the rib did not impact rib detection on radiographs. Left-sided rib fractures were not more common in NAT patients, compared to accidental trauma. SS miss approximately 17% of all rib fractures and CT is more sensitive modality in the detection of rib fractures, particularly acute rib fractures, regardless of location. Low-dose Chest CT could be a helpful modality in the work-up of NAT trauma.

Diagnostic value of chest computed tomography imaging for COVID-19 based on reverse transcription-polymerase chain reaction: a meta-analysis

BackgroundThe computed tomography (CT) diagnostic value of COVID-19 is controversial. We summarized the value of chest CT in the diagnosis of COVID-19 through a meta-analysis based on the reference standard.MethodsAll Chinese and English studies related to the diagnostic value of CT for COVID-19 across multiple publication platforms, was searched for and collected. Studies quality evaluation and plotting the risk of bias were estimated. A heterogeneity test and meta-analysis, including plotting sensitivity (Sen), specificity (Spe) forest plots, pooled positive likelihood ratio (+LR), negative likelihood ratio (-LR), dignostic odds ratio (DOR) values and 95% confidence interval (CI), were estimated. If there was a threshold effect, summary receiver operating characteristic curves (SROC) was further plotted. Pooled area under the receiver operating characteristic curve (AUROC) and 95% CI were also calculated.ResultsTwenty diagnostic studies that represented a total of 9004 patients were included from 20 pieces of literatures after assessing all the aggregated studies. The reason for heterogeneity was caused by the threshold effect, so the AUROC = 0.91 (95% CI: 0.89–0.94) for chest CT of COVID-19. Pooled sensitivity, specificity, +LR, -LR from 20 studies were 0.91 (95% CI: 0.88–0.94), 0.71 (95% CI: 0.59–0.80), 3.1(95% CI: 2.2–4.4), 0.12 (95% CI: 0.09–0.17), separately. The I2 was 85.6% (P = 0.001) by Q-test.ConclusionsThe results of this study showed that CT diagnosis of COVID-19 was close to the reference standard. The diagnostic value of chest CT may be further enhanced if there is a unified COVID-19 diagnostic standard. However, please pay attention to rational use of CT.Graphic

Additional Value of Non-contrast Chest CT in the Prediction of Adverse Cardiovascular Events in Patients With Novel Coronavirus Disease 2019 (COVID-19).

Background: Coronavirus disease 2019 (COVID-19) has outbroken in China and subsequently spread worldwide since the end of 2019. Chest computed tomography (CT) plays an important role in the diagnosis of lung diseases, but its value in the diagnosis of cardiac injury remains unknown.Methods: We enrolled 241 consecutive hospitalized patients (aged 61 ± 16 years, 115 males) with laboratory-confirmed COVID-19 at Renmin Hospital of Wuhan University from January 11 to March 2, 2020. They were divided into two groups according to whether major adverse cardiovascular events (MACEs) occurred during the follow-up. The anteroposterior diameter of the left atrium (LAD), the length of the left ventricle (LV), and cardiothoracic ratio (CTR) were measured. The values of myocardial CT were also recorded.Results: Of 241 patients, 115 patients (47.7%) had adverse cardiovascular events. Compared with no MACEs, patients with MACEs were more likely to have bilateral lesions (95.7% vs. 86.5%, p = 0.01). In multivariable analysis, bronchial wall thickening would increase the odds of MACEs by 13.42 (p = 0.01). LAD + LV and CTR was the best predictor for MACEs (area under the curve = 0.88, p < 0.001) with a sensitivity of 82.6% and a specificity of 80.2%. Plasma high-sensitivity troponin I levels in patients with cardiac injury showed a moderate negative correlation with minimum CT value (R2 = −0.636, p < 0.001).Conclusions: Non-contrast chest CT can be a useful modality for detection cardiac injury and provide additional value to predict MACEs in COVID-19 patients.

Descriptive Analysis of Chest Computed Tomography Scan in Coronavirus Disease 2019 Pneumonia: Correlation with Reverse Transcription-polymerase Chain Reaction and Clinical Features

BACKGROUND: Reverse transcriptase-polymerase chain reaction (RT-PCR) is the primary diagnostic tool to confirm coronavirus disease 2019 (COVID-2019) due to its high specificity. However, it has relatively low sensitivity and time consuming. In contrast, chest computed tomography (CT) has high sensitivity and achieves quick results. It may, therefore, play a critical role in screening and diagnosing COVID-19. A cross-sectional study was done in 212 patients with confirmed cases and patients under surveillance for COVID-19 tested for RT-PCR and chest CT scan. Statistical analysis was performed using SPSS Version 23 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA). AIM: We aim to investigate the diagnostic value of chest CT in correlation to RT-PCR in Indonesia. METHODS: A cross-sectional study was done in 212 patients with confirmed cases and patients under surveillance for COVID-19 tested for RT-PCR and chest CT scan. Statistical analysis was performed using SPSS Version 23 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA). RESULTS: From a total of 212 patients, 92% of them were diagnosed as confirmed cases of COVID-19. It was found that the sensitivity of CT scan for COVID-19 patients was 72.3% (65.5% and 78.5%) with positive predictive value (PPV) of 93.9% (90.9% and 96.0%) and the sensitivity and PPV improve in symptomatic patients. Typical chest CT scan lesions were 8.0 times which were more likely (3.9–16.4; p &lt;0.001) to be detected in symptomatic patients while patients with severe CT scan findings were 4.4 times more likely (3.0–6.5; p &lt;0.001) to be admitted to the intensive care unit. CONCLUSION: A high PPV suggests that a chest CT scan can detect COVID-19 lesions, but the absence of the lesions would not exclude the disease’s presence.

Chest Computed Tomography (CT) as a Predictor of Clinical Course in Coronavirus Disease

BackgroundChest imaging may be taken into consideration in detecting viral lung infections, especially if there are no tests available or there is a need for a prompt diagnosis. Imaging modalities enable evaluation of the character and extent of pulmonary lesions and monitoring of the disease course. The aim of this study was to verify the prognostic value of chest CT in COVID-19 patients.Material/MethodsWe conducted a retrospective review of clinical data and CT scans of 156 patients with SARS-CoV-2 infection confirmed by real-time reverse-transcription polymerase-chain-reaction (rRT-PCR) assay hospitalized in the Central Clinical Hospital of the Ministry of the Interior in Warsaw and in the Medical Centre in Łańcut, Poland. The total severity score (TSS) was used to quantify the extent of lung opacification in CT scans.ResultsThe dominant pattern in discharged patients was ground-glass opacities, whereas in the non-survivors, the dominant pulmonary changes were consolidations. The non-survivors were more likely to have pleural effusion, pleural thickening, lymphadenopathy, air bronchogram, and bronchiolectasis. There were no statistically significant differences among the 3 analyzed groups (non-survivors, discharged patients, and patients who underwent prolonged hospitalization) in the presence of fibrotic lesions, segmental or subsegmental pulmonary vessel enlargement, subpleural lines, air bubble sign, and halo sign.ConclusionsLung CT is a diagnostic tool with prognostic utility in COVID-19 patients. The correlation of the available clinical data with semi-quantitative radiological features enables evaluation of disease severity. The occurrence of specific radiomics shows a positive correlation with prognosis.

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome

PurposeTo evaluate the prognostic role of chest computed tomography (CT), alone or in combination with clinical and laboratory parameters, in COVID-19 patients during the first peak of the pandemic. MethodsA retrospective single-center study of 301 COVID-19 patients referred to our Emergency Department (ED) from February 25 to March 29, 2020. At presentation, patients underwent chest CT and clinical and laboratory examinations. Outcomes included discharge from the ED after improvement/recovery (positive outcome), or admission to the intensive care unit or death (poor prognosis). A visual quantitative analysis was formed using two scores: the Pulmonary Involvement (PI) score based on the extension of lung involvement, and the Pulmonary Consolidation (PC) score based on lung consolidation. The prognostic value of CT alone or integrated with other parameters was studied by logistic regression and ROC analysis. ResultsThe impact of the CT PI score [≥15 vs. ≤ 6] on predicting poor prognosis (OR 5.71 95 % CI 1.93−16.92, P = 0.002) was demonstrated; no significant association was found for the PC score. Chest CT had a prognostic role considering the PI score alone (AUC 0.722) and when evaluated with demographic characteristics, comorbidities, and laboratory data (AUC 0.841). We, therefore, developed a nomogram as an easy tool for immediate clinical application. ConclusionsVisual analysis of CT gives useful information to physicians for prognostic evaluations, even in conditions of COVID-19 emergency. The predictive value is increased by evaluating CT in combination with clinical and laboratory data.

Should CT be used for the diagnosis of RT-PCR-negative suspected COVID-19 patients?

IntroductionThe diagnosis of patients with Coronavirus disease 2019 (COVID‐19) suspicion but negative reverse transcriptase‐polymerase chain reaction (RT‐PCR) test is challenging.ObjectiveWe aimed to investigate the diagnostic value of chest computed tomography (CT) in RT‐PCR‐negative patients with suspected COVID‐19.Materials and methodsThe study included patients who were admitted to our hospital with the suspicion of COVID‐19 between 1 April 2020 and 30 April 2020 and tested negative after RT‐PCR test, and underwent CT for further diagnosis. Initial CT findings were classified as typical, indeterminate, and atypical for COVID‐19, and negative for pneumonia. Incidental findings on CT were noted.ResultsOf the 338 patients with a mean age of 57 years (min 18 years–max 96 years), 168 (49.70%) were male and 170 (50.29%) were female. The most common symptoms were cough (58.87%), fever (40.82%), and dyspnea (39.34%). The CT findings were typical for COVID‐19 in 109 (32.24%) patients, indeterminate in 47 (13.90%) patients, and atypical in 77 (22.78%) patients. The CT findings of 105 (31.06%) patients were negative for pneumonia. Incidental lung nodules suspicious of malignancy were identified in seven patients. Seventy‐seven patients (22.78%) had extrapulmonary incidental findingsConclusionThe diagnostic value of CT in RT‐PCR‐negative patients with suspected COVID‐19 is not very high. Based on clinical, laboratory, and chest x‐ray findings, it may be more appropriate to refer patients to CT after the first triage, when necessary.