Chest Chest Computed Tomography Images Research Articles

Chest Computed Tomography Radiomics for Determining Macrolide Resistance-Associated Gene Mutation Status in Children with Mycoplasma pneumoniae Pneumonia: A Two-Center Study

Rationale and ObjectivesThe mutations in the 23S ribosomal RNA (rRNA) gene are associated with an increase in resistance to macrolides in children with Mycoplasma pneumoniae pneumonia (MPP). This study aimed to develop and validate a chest computed tomography (CT) radiomics model for determining macrolide resistance-associated gene mutation status in MPP. Materials and MethodsA total of 258 MPP patients were retrospectively included from two institutions (training set: 194 patients from the first institution; external test set: 64 patients from the second). The 23S rRNA gene mutation status was tested by nasopharyngeal swab polymerase chain reaction. Radiomics features were extracted from chest CT images of pulmonary lesions segmented with semi-automatic delineation. Subsequently, radiomics feature reduction was applied to identify the most relevant features. Logistic regression and random forest algorithms were employed to establish the radiomics models, which were five-fold cross-validated in the training set and validated in the external test set. ResultsThe radiomics feature selection resulted in eight features. After five-fold cross-validation in the training set, the mean areas under the receiver operating characteristic curve (AUCs) of the logistic regression and random forest models were 0.868 (95% confidence interval (CI): 0.813-0.923) and 0.941 (95% CI: 0.907-0.975), respectively. In the external test set, the corresponding AUCs were 0.855 (95% CI: 0.758-0.952) and 0.815 (95% CI: 0.705-0.925). ConclusionChest CT radiomics is a promising diagnostic tool for determining macrolide resistance gene mutation status in MPP. Availability of data and materialThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia

BackgroundThe identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML).Methods952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements.ResultsThree CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011).ConclusionsIn summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Chest CT Imaging Features of HIV/SARS-COV-2 Infected Patients from the Capital of Amazonas State

The impact of the COVID-19 pandemic caused by SARS-CoV-2 on global public health, emphasizing its severity and the efforts made to enhance diagnostic practices and clinical management. Chest Computed Tomography (CT) has been widely used to assess complications caused by the virus, particularly in patients coinfected with HIV. The study evaluated 29 chest CT scans, comparing HIV+ and HIV- patients. Among HIV+ patients, 66.7% reported regular antiretroviral therapy, and 43.8% had a CD4+ T cell count below 200 cells/mm3 . Coinfections were observed, including neurotoxoplasmosis and pulmonary tuberculosis. Clinical outcomes showed that 90% of patients were discharged, 3.5% were transferred, and 7% died, all of whom were HIV+ . CT findings revealed that HIV+ patients exhibited fewer ground-glass opacities, but a higher rate of consolidation. The severity score was lower in HIV+ patients, with the lower lobes of HIV- patients more affected. The text also discusses the variability in CT manifestations in HIV patients, highlighting the importance of considering previous conditions. The study suggests that HIVrelated immunosuppression may paradoxically protect against severe COVID-19 manifestation, but low CD4 counts (&lt;200 cells/ µL) can predispose patients to more severe forms of the disease. However, the study acknowledges limitations, such as a small sample size and the need for follow-up CT examinations to understand the complete disease course.

U-TranSvision: Transformer-based deep supervision approach for COVID-19 lesion segmentation on Computed Tomography images

Artificial intelligence (AI)-assisted COVID-19 detection in chest computed tomography (CT) images has an important role in the early diagnosis and appropriate treatment of infected patients. Convolutional neural network based AI approaches have shown significant performance in segmenting COVID-19 lesion regions. However, they have several limitations to deal with the complexity of the lesion characteristics, low or high image contrast, and small lesion regions. To address these limitations, we propose a novel architecture called U-TranSvision, which leverages transformers and deep supervision to improve segmentation performance by focusing on the salient features of small COVID-19 lesions. Furthermore, Pix2Pix generative adversarial network was used in data augmentation to improve the performance of U-TranSvision, and pre-processing steps were applied to remove the noise around human tissue on an image. In addition, we created a relatively large-scale dataset of 11,717 axial chest CT images, along with their corresponding pixel-level annotations. Based on extensive experimental evaluations, U-TranSvision achieved a dice similarity coefficient of 85.57% and an intersection over Union of 74.82%. The experiments were also conducted on three publicly available datasets, such as COVID-19-CT-Seg, MosMedData, and MedSeg, to demonstrate the robustness of U-TranSvision. The qualitative and quantitative results proved that U-TranSvision had promising performance compared to the state-of-the-art architectures for COVID-19 lesion segmentation. In addition, U-TranSvision has relatively low learning parameters, which results in low computational costs.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Artificial intelligence-based quantification of COVID-19 pneumonia burden using chest CT

AbstractDuring the coronavirus disease 2019 (COVID-19) pandemic, artificial intelligence (AI) based software on chest computed tomography (CT) imaging has proven to have a valuable role in accelerating diagnosis and screening. The proposed AI-based tools proved to be rapid and reproducible techniques to guide patient management and treatment protocols. Although no specific guidelines exist, CT-imaging and clinical features are used for patient staging. To shed light on the role of AI techniques that have been developed in fighting COVID-19, in this review, studies investigating the usage of commonly used AI models on chest CT imaging for disease quantification and prognostication are collected.

Self-attention-driven retrieval of chest CT images for COVID-19 assessment

Numerous methods have been developed for computer-aided diagnosis (CAD) of coronavirus disease-19 (COVID-19), based on chest computed tomography (CT) images. The majority of these methods are based on deep neural networks and often act as “black boxes” that cannot easily gain the trust of medical community, whereas their result is uniformly influenced by all image regions. This work introduces a novel, self-attention-driven method for content-based image retrieval (CBIR) of chest CT images. The proposed method analyzes a query CT image and returns a classification result, as well as a list of classified images, ranked according to similarity with the query. Each CT image is accompanied by a heatmap, which is derived by gradient-weighted class activation mapping (Grad-CAM) and represents the contribution of lung tissue and lesions to COVID-19 pathology. Beyond visualization, Grad-CAM weights are employed in a self-attention mechanism, in order to strengthen the influence of the most COVID-19-related image regions on the retrieval result. Experiments on two publicly available datasets demonstrate that the binary classification accuracy obtained by means of DenseNet-201 is 81.3% and 96.4%, for COVID-CT and SARS-CoV-2 datasets, respectively, with a false negative rate which is less than 3% in both datasets. In addition, the Grad-CAM-guided CBIR framework slightly outperforms the plain CBIR in most cases, with respect to nearest neighbour (NN) and first four (FF). The proposed method could serve as a computational tool for a more transparent decision-making process that could be trusted by the medical community. In addition, the employed self-attention mechanism increases the obtained retrieval performance.

Patients With Post-COVID-19 Respiratory Condition: Chest Computed Tomography Findings and Pulmonary Function Tests and Comparison With Asymptomatic Participants.

The aims of this study were to assess the chest computed tomography (CT) findings in post-COVID-19 respiratory condition (rPCC) patients and compare the findings with asymptomatic participants (APs). It also aimed to evaluate the relationship between CT findings and pulmonary function tests (PFTs) in rPCC patients. Finally, it aimed to compare the quantitative chest CT findings and PFT results of patients with rPCC and APs. We retrospectively enrolled consecutive patients with rPCC who underwent unenhanced chest CT and PFTs between June 2020 and September 2022. In addition, a control group (APs) was prospectively formed and underwent nonenhanced chest CT and PFTs. The presence and extent of abnormalities in unenhanced chest CT images were evaluated qualitatively and semiquantitatively in a blinded manner. We used fully automatic software for automatic lung and airway segmentation and quantitative analyses. Sixty-three patients with rPCC and 23 APs were investigated. Reticulation/interstitial thickening and extent of parenchymal abnormalities on CT were significantly greater in the rPCC group than in the control group ( P = 0.001 and P = 0.004, respectively). Computed tomography extent score was significantly related to length of hospital stay, age, and intensive care unit stay (all P s ≤ 0.006). The rPCC group also had a lower 85th percentile attenuation lung volume ( P = 0.037). The extent of parenchymal abnormalities was significantly correlated with carbon monoxide diffusing capacity ( r = -0.406, P = 0.001), forced vital capacity (FVC) ( r = -0.342, P = 0.002), and forced expiratory volume in 1 second/FVC ( r = 0.427, P < 0.001) values. Pulmonary function tests revealed significantly lower carbon monoxide diffusing capacity ( P < 0.001), FVC ( P = 0.036), and total lung capacity ( P < 0.001) values in the rPCC group. The rPCC is characterized by impaired PFTs, a greater extent of lung abnormalities on CT, and decreased 85th percentile attenuation lung volume. Advanced age, intensive care unit admission history, and extended hospital stay are risk factors for chest CT abnormalities.

Optical Biopsies Using Probe-Based Confocal Laser Endomicroscopy for Autoimmune Pulmonary Alveolar Proteinosis

Introduction: Increasing numbers of cases of mild asymptomatic pulmonary alveolar proteinosis (PAP) are being reported with the recent increase in chest computed tomography (CT). Bronchoscopic diagnosis of mild PAP is challenging because of the patchy distribution of lesions, which makes it difficult to obtain sufficient biopsy samples. Additionally, the pathological findings of mild PAP, particularly those that differ from severe PAP, have not been fully elucidated. This study aimed to clarify the pathological findings of mild PAP and the usefulness of optical biopsy using probe-based confocal laser endomicroscopy (pCLE). Methods: We performed bronchoscopic optical biopsy using pCLE and tissue biopsy in 5 consecutive patients with PAP (three with mild PAP and two with severe PAP). We compared the pCLE images of mild PAP with those of severe PAP by integrating clinical findings, tissue pathology, and chest CT images. Results: pCLE images of PAP showed giant cells with strong fluorescence, amorphous substances, and thin alveolar walls. Images of affected lesions in mild PAP were equivalent to those obtained in arbitrary lung lesions in severe cases. All 3 patients with mild PAP spontaneously improved or remained stable after ≥3 years of follow-up. Serum autoantibodies to granulocyte-macrophage colony-stimulating factor were detected in all 5 cases. Conclusion: Optical biopsy using pCLE can yield specific diagnostic findings, even in patients with mild PAP. pCLE images of affected areas in mild and severe PAP showed similar findings, indicating that the dysfunction level of pathogenic alveolar macrophages in affected areas is similar between both disease intensities.

Can chest computed tomography be useful to emergency residents in the diagnosis of COVID-19 pneumonia?

&lt;b&gt;Aim:&lt;/b&gt; During the novel coronavirus disease (COVID-19) pandemic, the workload of emergency department residents (ER) has increased since emergency departments (EDs) are among the first health units to which patients apply. The aim of this study was to investigate the diagnostic value of chest computed tomography (CT) interpretations of emergency department residents in the diagnosis of COVID-19 pneumonia.&lt;br /&gt; &lt;b&gt;Material and methods: &lt;/b&gt;Patients who admitted to the tertiary ED between 30.03.2020 and 30.12.2020 with suspected COVID-19 pneumonia and underwent chest CT imaging were retrospectively analyzed. Chest CT images previously reported by the radiologist were reinterpreted by the ER and the diagnostic performance of the ER for COVID-19 pneumonia was calculated.&lt;br /&gt; &lt;b&gt;Results: &lt;/b&gt;The study included 2045 patients with suspected COVID-19 pneumonia. Chest CT images of all patients were interpreted and compared by a radiologist and the ER In this study, interpretation by a radiologist was considered as the gold standard. In all patients, the diagnostic performance capacity of ER for COVID-19 pneumonia included a low sensitivity of 66%, a high specificity of 92%, a positive predictive value (PPV) of 83% and a negative predictive value (NPV) of 82%. When patients with positive COVID-19 RT-PCR test results were analyzed, the highest sensitivity, specificity, PPV and NPV of ER were 96%, 82%, 95%, 95% and 86%, respectively.&lt;br /&gt; &lt;b&gt;Conclusion: &lt;/b&gt;ER detected COVID-19 pneumonia in COVID-19 (+) patients with high diagnostic value and excluded inconsistent findings. Chest CT interpretation in the ER has a high diagnostic value. It is as important as any other radiologist in outbreak management in the ED.

Retrospective computed tomography assessment of chemotherapy-related pneumonia with severity screening in pediatric acute lymphoblastic leukemia by radiological imaging

ObjectivesTo evaluate the radiological imaging-guided severity along the pneumonia course and evaluate the chest computed tomography (CT) findings of chemotherapy-related pneumonia in children with acute lymphoblastic leukemia (ALL). Materials and methodsA retrospective database review of children with ALL was conducted from March 2016 to August 2021 to identify cases with CT images who developed pneumonia during the chemotherapy course. A total of 51 children with ALL developed pneumonia were ultimately included (31 boys and 20 girls, mean age: 6 ± 4 years [standard deviation]). Each child's demographics, medical records, and laboratory results were collected. The CT images were then reviewed and the radiologic severity index (RSI) was calculated based on the regional opacity and implicated volume. A t-test, U test, Pearson's Chi-square test, and Fisher's exact test were performed to compare the clinical or radiologic features between the severe and moderate cases. The linear regression models were employed to analyze the correlation of RSIs with other clinical features. ResultsEleven children (22 %, 11/51) displayed severe phenotypes associated with respiratory failure. The ground glass opacity (GGO) frequently appeared (65 % of CT images). The baseline RSI was positively associated with the lowest lymphocyte (p = .003), neutrophil (p = .01) counts, and the highest C-reactive protein level (p = .04). The peak RSI may predict severe phenotypes at a cutoff of 4.5 (AUC 0.76 [0.61, 0.91]) with 73 % sensitivity and 63 % specificity. ConclusionThe chest CT images of children with chemotherapy-related pneumonia displayed clinically related baseline RSI and a peak RSI of >4.5 of 36 predicted severe phenotypes.

Automatic chest computed tomography image noise quantification using deep learning

PurposeThis study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions. MethodsA comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps. ResultsNoise SD maps predicted by the CNN agreed well with the ground truth both visually and numerically in the phantom dataset (errors of < 5 HU for most scan parameter combinations). In addition, the noise SD estimates obtained from clinical chest CT images were similar to running-average based reference estimates in areas without prominent tissue interfaces. ConclusionsPredicting local noise magnitudes without the need for repeated scans is feasible using DL. Our implementation trained with phantom data was successfully applied to open-source clinical data with heterogeneous tissue borders and textures. We suggest that automatic DL noise mapping from clinical patient images could be used as a tool for objective CT image quality estimation and protocol optimization.

Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules.

The prediction of the persistent pure ground-glass nodule (pGGN) growth is challenging and limited by subjective assessment and variation across radiologists. A chest computed tomography (CT) image-based deep learning classification model (DLCM) may provide a more accurate growth prediction. This retrospective study enrolled consecutive patients with pGGNs from January 2010 to December 2020 from two independent medical institutions. Four DLCM algorithms were built to predict the growth of pGGNs, which were extracted from the nodule areas of chest CT images annotated by two radiologists. All nodules were assigned to either the study, the inner validation, or the external validation cohort. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUROCs) were analyzed to evaluate our models. A total of 286 patients were included, with 419 pGGN. In total, 197 (68.9%) of the patients were female and the average age was 59.5±12.0 years. The number of pGGN assigned to the study, the inner validation, and the external validation cohort were 193, 130, and 96, respectively. The follow-up time of stable pGGNs for the primary and external validation cohorts were 3.66 (range, 2.01-10.08) and 4.63 (range, 2.00-9.91) years, respectively. Growth of the pGGN occurred in 166 nodules [83 (43%), 39 (30%), and 44 (45%) in the study, inner and external validation cohorts respectively]. The best-performing DLCM algorithm was DenseNet_DR, which achieved AUROCs of 0.79 [95% confidence interval (CI): 0.70, 0.86] in predicting pGGN growth in the inner validation cohort and 0.70 (95% CI: 0.60, 0.79) in the external validation cohort. DLCM algorithms that use chest CT images can help predict the growth of pGGNs.

The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia.

Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80-8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23-4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.

The alveolar fibroproliferative response in moderate to severe COVID-19-related acute respiratory distress syndrome and 1-yr follow-up.

COVID-19-related acute respiratory distress syndrome (ARDS) can lead to long-term pulmonary fibrotic lesions. Alveolar fibroproliferative response (FPR) is a key factor in the development of pulmonary fibrosis. N-terminal peptide of procollagen III (NT-PCP-III) is a validated biomarker for activated FPR in ARDS. This study aimed to assess the association between dynamic changes in alveolar FPR and long-term outcomes, as well as mortality in COVID-19 ARDS patients. We conducted a prospective cohort study of 154 COVID-19 ARDS patients. We collected bronchoalveolar lavage (BAL) and blood samples for measurement of 17 pulmonary fibrosis biomarkers, including NT-PCP-III. We assessed pulmonary function and chest computed tomography (CT) at 3 and 12 mo after hospital discharge. We performed joint modeling to assess the association between longitudinal changes in biomarker levels and mortality at day 90 after starting mechanical ventilation. 154 patients with 284 BAL samples were analyzed. Of all patients, 40% survived to day 90, of whom 54 completed the follow-up procedure. A longitudinal increase in NT-PCP-III was associated with increased mortality (HR 2.89, 95% CI: 2.55-3.28; P < 0.001). Forced vital capacity and diffusion for carbon monoxide were impaired at 3 mo but improved significantly at one year after hospital discharge (P = 0.03 and P = 0.004, respectively). There was no strong evidence linking alveolar FPR during hospitalization and signs of pulmonary fibrosis in pulmonary function or chest CT images during 1-yr follow-up. In COVID-19 ARDS patients, alveolar FPR during hospitalization was associated with higher mortality but not with the presence of long-term fibrotic lung sequelae within survivors.NEW & NOTEWORTHY This is the first prospective study on the longitudinal alveolar fibroproliferative response in COVID-19 ARDS and its relationship with mortality and long-term follow-up. We used the largest cohort of COVID-19 ARDS patients who had consecutive bronchoalveolar lavages and measured 17 pulmonary fibroproliferative biomarkers. We found that a higher fibroproliferative response during admission was associated with increased mortality, but not correlated with long-term fibrotic lung sequelae in survivors.

Longitudinal Association between L1 Trabecular Attenuation from Chest Computed Tomography (CT) and Bone Mineral Density from Dualenergy X-ray Absorptiometry (DXA).

Many studies have shown that vertebral trabecular attenuation measured on CT scan corresponds well to DXA results for bone mineral density. These studies were based on crosssectional data. Hence, there were limitations in explaining the constantly changing vertebral trabecular attenuation from CT and T-score from DXA over time. This study aimed to determine the longitudinal association between the vertebral trabecular attenuation measured on computed tomography (CT) and the T-score measured by dual-energy X-ray absorptiometry (DXA). We performed a database search for 333 patients who underwent surgery for breast cancer, preoperative treatment, and at least one follow-up chest CT and DXA from January, 2013 through May, 2021. One musculoskeletal radiologist measured the mean vertebral trabecular attenuation of lumbar vertebra 1(L1) on axial unenhanced images at the pedicle level by manually placing the region of interest (ROI). DXA of the lumbar spine was performed, and the lowest T-score of the lumbar spine was used for the analysis. We evaluated the association between L1 trabecular attenuation from chest CT and T-score from DXA over time using the generalized estimating equations (GEE) model to analyze longitudinal corrected data. A total of 150 women (mean age, 52.4 ± 11.0 years) were included. There was a statistically significant association between L1 trabecular attenuation from chest CT and T-score from DXA in the unadjusted model (p < 0.001) and adjusted model (p < 0.001). T-score value increased by 0.172 (95% confidence interval (CI): 0.145-0.200, p < 0.001) per 10 unit (HU) of L1 trabecular attenuation at time = 0 in unadjusted model and by 0.173 (95% CI: 0.143-0.203, p < 0.001) in all adjusted model. We demonstrated that L1 attenuation from chest CT images was longitudinally associated with T-score from DXA, and the degree of association appeared to be decreased over time in breast cancer patients regardless of their medical condition.

Pulmonary embolism induces pneumonia-like lung injury beyond pulmonary infarction.

Patients with pulmonary embolism (PE) commonly manifest concomitant "pneumonia," which is generally believed to be either a cause (infection) or a consequence (infarction) of PE. This study aimed to clarify the relationship between PE and "pneumonia-like" lesions beyond pulmonary infection and infarction. Chest computed tomography (CT) images of patients with PE and deep vein thrombosis (DVT) were retrospectively analyzed to compare the incidence of pneumonia lesions. The pathological damage and wet/dry ratio of lung tissues were observed in PE rats and PE plasma-injected rats. In total, 793 and 914 inpatients were enrolled in the PE and DVT groups, respectively. Pneumonia lesions were observed in 36.9% and 26.3% of patients in the PE and DVT groups, respectively (p < 0.0001). Among PE rats, 33.3% exhibited focal severe lung injury, which closely resembled the pathological damage of community-acquired pneumonia. The wet/dry ratio was significantly higher in the PE group than in the PE-control group (4.98 ± 0.08 vs. 4.39 ± 0.06, p < 0.0001). Among PE plasma-injected rats, individuals with focal proven lung injury were found at all experimental points, with an incidence of 27.6%. The lung wet/dry ratio was significantly higher in the PE plasma group than in the PE-control plasma group at 1 and 2 h postinjection (5.02 ± 0.12 vs. 4.61 ± 0.06 and 4.76 ± 0.16 vs. 4.34 ± 0.09, respectively; p < 0.05). In conclusion, the manifestation of pneumonia lesions in chest CT images was higher among PE patients than among DVT patients. Plasma of PE rats could induce focal pneumonia-like lung injury in healthy rats.

Existing Nongated CT Coronary Calcium Predicts Operative Risk in Patients Undergoing Noncardiac Surgeries (ENCORES).

Preoperative cardiovascular risk stratification before noncardiac surgery is a common clinical challenge. Coronary artery calcium scores from ECG-gated chest computed tomography (CT) imaging are associated with perioperative events. At the time of preoperative evaluation, many patients will not have had ECG-gated CT imaging, but will have had nongated chest CT studies performed for a variety of noncardiac indications. We evaluated relationships between coronary calcium severity estimated from previous nongated chest CT imaging and perioperative major clinical events (MCE) after noncardiac surgery. We retrospectively identified consecutive adults age ≥45 years who underwent in-hospital, major noncardiac surgery from 2016 to 2020 at a large academic health system composed of 4 acute care centers. All patients had nongated (contrast or noncontrast) chest CT imaging performed within 1 year before surgery. Coronary calcium in each vessel was retrospectively graded from absent to severe using a 0 to 3 scale (absent, mild, moderate, severe) by physicians blinded to clinical data. The estimated coronary calcium burden (ECCB) was computed as the sum of scores for each coronary artery (0 to 9 scale). A Revised Cardiac Risk Index was calculated for each patient. Perioperative MCE was defined as all-cause death or myocardial infarction within 30 days of surgery. A total of 2554 patients (median age, 68 years; 49.7% women; median Revised Cardiac Risk Index, 1) were included. The median time interval from nongated chest CT imaging to noncardiac surgery was 15 days (interquartile range, 3-106 days). The median ECCB was 1 (interquartile range, 0-3). Perioperative MCE occurred in 136 (5.2%) patients. Higher ECCB values were associated with stepwise increases in perioperative MCE (0: 2.9%, 1-2: 3.7%, 3-5: 8.0%; 6-9: 12.6%, P<0.001). Addition of ECCB to a model with the Revised Cardiac Risk Index improved the C-statistic for MCE (from 0.675 to 0.712, P=0.018), with a net reclassification improvement of 0.428 (95% CI, 0.254-0.601, P<0.0001). An ECCB ≥3 was associated with 2-fold higher adjusted odds of MCE versus an ECCB <3 (adjusted odds ratio, 2.11 [95% CI, 1.42-3.12]). Prevalence and severity of coronary calcium obtained from existing nongated chest CT imaging improve preoperative clinical risk stratification before noncardiac surgery.

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.

Occult pneumothorax in patients with blunt chest trauma: key findings on supine chest radiography.

In patients with multiple trauma, a supine chest radiography [chest X-ray (CXR)] is preferred over a erect CXR. However, this method has limitations in detecting post-traumatic pneumothorax. The use of chest computed tomography (CT) to detect traumatic pneumothorax is well known. However, pneumothorax that is not detected before a chest CT scan is known as an occult pneumothorax (OP), and it can cause serious complications in the patient. This study sought to evaluate the frequency and risk factors for OP in trauma patients. Patients who suffered thoracic trauma at the Level 1 Regional Trauma Center of Wonju Severance Christian Hospital between 2015 and 2022 were included in this study. All patients were at least 18 years old. The study reviewed all patients' supine CXR and chest CT images and classified them into five radiographic diagnoses: pneumothorax, rib fracture, subcutaneous emphysema, lung contusion, and pneumomediastinum. The study included 1,284 patients, all with diagnoses of pneumothorax, rib fracture, subcutaneous emphysema, lung contusion, and pneumomediastinum following supine CXR and chest CT. The patient's average age was 58.3±15.2 years. Pneumothorax diagnosis on supine CXR had the lowest accuracy, at 46.7%, and the lowest sensitivity, at 12.7%. In univariate analysis, rib fracture, lung contusion, and subcutaneous emphysema on supine CXR were all found to be statistically significant regarding traumatic OP. In multivariate analysis, the risk factors for OP were lung contusion [odds ratio (OR), 1.440; 95% confidence interval (CI): 1.115-1.860; P=0.005] and subcutaneous emphysema (OR, 25.883; 95% CI: 13.155-50.928; P<0.001) on supine CXR. The lung contusion and subcutaneous emphysema in supine CXR of trauma patients indicate the presence of OP. Therefore, if chest CT cannot be performed immediately due to unstable vital signs or other circumstances, recognizing the above radiological findings of traumatic pneumothorax may be necessary.