Chest Chest Computed Tomography Images Research Articles

Utilizing radiomics techniques to isolate a single vertebral body from chest CT for opportunistic osteoporosis screening

PurposeOpportunistic osteoporosis screening, conducted during routine medical examinations such as chest computed tomography (CT), presents a potential solution for early detection. This study aims to investigate the feasibility of utilizing radiomics technology based on chest CT images to screen for opportunistic osteoporosis.MethodsThis Study is a Multicenter Retrospective Investigation. Relevant clinical data, including demographics and DXA results, would be collected for each participant. The radiomics analysis in this study focuses on the extraction of features from the 11th or 12th thoracic vertebral bodies from chest CT images. SVM machine learning models would be trained using these radiomic features, with DXA results as the ground truth for osteoporosis classification.ResultsIn the training group, Clinical models had an accuracy of 0.684 and an AUC of 0.744, Radiomics models had an accuracy of 0.828 and an AUC of 0.896, Nomogram models had an accuracy of 0.839 and an AUC of 0.901. In the internal validation group, Clinical models had an accuracy of 0.769 and an AUC of 0.829, Radiomics models had an accuracy of 0.832 and an AUC of 0.892, Nomogram models had an accuracy of 0.839 and an AUC of 0.918. In the external validation group, Clinical models had an accuracy of 0.715 and an AUC of 0.741, Radiomics models had an accuracy of 0.777 and an AUC of 0.796, Nomogram models had an accuracy of 0.785 and an AUC of 0.807. In all three datasets, the Nomogram model exhibited a statistically significant difference in screening effectiveness compared to the clinical models.ConclusionOur research demonstrates that by leveraging radiomics features extracted from a single thoracic spine using chest CT, and incorporating these features with patient basic information, opportunistic screening for osteoporosis can be achieved.

Can lung anatomy predict the development of COVID-19 pneumonia in RT-PCR positive cases?

Purpose: The objective of this study was to investigate the relationship between Coronavirus disease 2019 (COVID-19) pneumonia and anatomical characteristics, including tracheobronchial and fissure variations, right/left main bronchus angle, carina angle, and large airway diameter, in patients who tested positive for the virus via reverse transcription-polymerase chain reaction (RT-PCR) at the outset of the pandemic. Materials and Methods: This cross-sectional study included 165 cases with positive RT-PCR tests who were admitted between March and June 2020 and subsequently scanned with thin-section unenhanced chest computed tomography (CT). They were divided into two groups according to the presence of pneumonia based on the chest CT images. Results: In our study, a total of 165 cases were analyzed, pneumonia was found in 35 of 76 (46.05%) female patients and 51 of 89 (57.30%) male patients. COVID-19 pneumonia was observed in older age groups. Anatomical variations and the presence of incomplete fissures were statistically higher in the pneumonia-positive group, while the difference was not statistically significant for accessory fissures. Mean tracheal area and right main bronchus angle were statistically higher in the pneumonia-positive group compared to the negative patients. The right lung was more involved than the left in terms of both zonal and total lung involvement. When the presence of comorbidity was assessed, 59 patients (35.75%) were found to have comorbidity. Hypertension, diabetes mellitus and cardiovascular disease were significantly higher among comorbidities in the pneumonia-positive group. The regression model showed that hypertension was associated with a 3.75-fold increase in COVID-19 pneumonia in test positive cases. Conclusion: Anatomical lung variations and incomplete fissures were observed more frequently in the COVID-19 pneumonia cases, independent of other comorbidities. We believe that there are anatomical variables that can be used to identify those predisposed to pneumonia in RT-PCR test positive cases.

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited. This study aimed to develop and validate a vision transformer-based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging. This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT-based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT-based DTL signature was combined with independent clinical predictors to construct a ViT-based deep transfer learning nomogram (DTLN). The ViT-based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775-0.867) in the training cohort and 0.825 (95% CI, 0.758-0.891) in the validation cohort. The ViT-based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT-based DTL signature was then used to construct ViT-based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827-0.903) and 0.894 (95% CI, 0845-0942), respectively, surpassing both the clinical factor model and the ViT-based DTL model (p<0.001). This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

Feasibility of Epicardial Adipose Tissue Quantification Using Non-electrocardiogram-Gated Chest Computed Tomography Images.

Epicardial adipose tissue (EAT) is an important imaging indicator of cardiovascular risk. EAT volume is usually measured using electrocardiogram (ECG) gating. However, there are concerns regarding the influence of motion artifacts when measuring EAT volume on non-ECG-gated plain chest computed tomography (CT) images. Few studies have evaluated the EAT volume using non-ECG gating. This study aimed to validate the accuracy of EAT quantification using non-ECG-gated chest CT imaging. We included 100 patients (64 males, 36 females) who underwent simultaneous coronary artery calcification score imaging (ECG gated) and plain chest CT imaging (non-ECG gated). Images taken using non-ECG gating were reconstructed using the same field of view and slice thickness as those obtained with ECG gating. The EAT capacity of each image was measured and compared. An AZE Virtual Place (Canon) was used for the measurements. The Mann-Whitney U test and intraclass correlation coefficient were used for statistical analyses. P values <0.05 were considered statistically significant. Concordance was evaluated using Bland-Altman analysis. The mean EAT volume measured by ECG-gated imaging was 156.5 ± 66.9 mL and 155.4 ± 67.9 mL by non-ECG-gated imaging, with no significant difference between the two groups (P = 0.86). Furthermore, the EAT volumes measured using ECG-gated and non-ECG-gated imaging showed a strong correlation (r = 0.95, P < 0.05). Bland-Altman analysis revealed that the mean error of the EAT volume (non-ECG-gated imaging - ECG-gated imaging) was -1.02 ± 2.95 mL (95% confidence interval, -6.49 to 4.76). The EAT volume obtained using non-ECG-gated imaging was equivalent to that obtained using ECG-gated imaging.

Balancing accuracy and efficiency: A lightweight deep learning model for COVID 19 detection

The novel coronavirus disease in human was detected in Wuhan, China and spread rapidly across countries each time mutating itself as a new variant. It has impacted not only the patients but also medical infrastructure a lot. Several artificial intelligence-based systems for detection of covid-19 in chest images have been proposed. This study aims to develop a deep learning-based approach that efficiently detect Covid-19 using chest Computed Tomography (CT) images. In this work, we propose an efficient and lightweight deep-learning architecture based on the concept of a densely connected network for Covid-19 detection. The proposed architecture is designed by stacking dense modules in which each layer is directly connected to the other layers, with very fewer number of neurons at each layer. The dense connection is used to improve the learning capability of the proposed model and the fewer number of neurons make network computationally efficient. CT images for covid-19 detection are effectively used for the detection of Covid-19 patients. We train and test our model on SARS-CoV-2 CT-scan datasets. The experimental results are evaluated on accuracy, sensitivity and F1 score. The Adam optimizer with binary cross entropy loss function gives 97.2% accuracy with 0.98 sensitivity, 0.97 precision and 0.97 F1 score. The model achieves maximum accuracy of 88% at precision 0.97%, specificity 0.82 and F1 score 0.89 with Covid-CT dataset. The proposed model is compared with other state-of-the-art methods. The results of the proposed methods and its comparison shows acceptable level of performance on chest CT images.

Utility of block-matching and 3D filter for reproducibility of lung density and denoising in low-dose chest CT: A pilot study

PurposeThis study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma. MethodsUsing full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level. ResultsThe 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose = − 926.28 ± 0.32, BM3D = − 926.65 ± 0.32, and low-dose = − 959.43 ± 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05). ConclusionsThe results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.

Employing Computed Tomography to Assess Clavicle Symmetry in Healthy Adults in the Indian Population of Dakshina Karnataka

Abstract Background The S-shaped collar bone lies horizontally at the root of the neck and is one of the most common bones to fracture. The symmetry of the clavicle bone differs between males and females, so the present study was conducted to assess the symmetry of the clavicle in healthy adults of the Indian population in the Dakshina Karnataka region. Methods The data for this cross-sectional study were retrospectively collected from 138 adult patients who all underwent chest computed tomography (CT) for clinical reasons. Patients younger than 18 years and those with bony fractures, trauma, or other deformities were excluded. Axial chest CT images were used to trace the anatomy of the clavicle. The curved planar reconstruction technique was used to trace the maximum length of the bilateral clavicle. Results There were 138 patients of both sexes. Patients ranged in age from 20 to 80 years. The chi-square test was used to investigate whether sex and clavicle symmetry were related. In women, bilateral symmetry and asymmetry were 42.5 and 41.2%, respectively, while in men, they were 57.5 and 58.8%, respectively; hence, there was no significant correlation (p-value = 0.877) between clavicle symmetry and sex. A linear-by-linear association test was performed to ascertain the relationship between the variance in clavicle symmetry and age. It was noted that 51 individuals had asymmetry and 87 had symmetry. The patients were further divided into three age groups. There were 34, 67, and 37 individuals in the 20 to 40, 41 to 60, and 61 to 80 years age groups, respectively. Clavicle symmetry was 27.6% in the 20 to 40 and 61 to 80 age groups and 44.8% in the 41 to 60 age group. The percentages of asymmetry were 19.6, 54.9, and 25.5% in the 20 to 40, 41 to 60, and 61 to 80 years age groups, respectively, with no correlation between age and clavicle length symmetry (p-value = 0.643). Conclusion Regardless of age or sex, the population under study had symmetric clavicles. As a result, the assumption of symmetry may be accepted in some clinical scenarios, such as simple fractures, but caution should be exercised when designing fixatives and surgical cases.

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.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

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.

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.

Omicron variant and pulmonary involvements: a chest imaging analysis in asymptomatic and mild COVID-19.

To identify clinical characteristics and risk factors for pulmonary involvements in asymptomatic and mildly symptomatic patients infected with SARS-CoV-2 Omicron variant by chest imaging analysis. Detailed data and chest computed tomography (CT) imaging features were retrospectively analyzed from asymptomatic and mildly symptomatic patients infected with Omicron between 24 April and 10 May 2022. We scored chest CT imaging features and categorized the patients into obvious pulmonary involvements (OPI) (score > 2) and not obvious pulmonary involvements (NOPI) (score ≤ 2) groups based on the median score. The risk factors for OPI were identified with analysis results visualized by nomogram. In total, 339 patients were included (145 were male and 194 were female), and the most frequent clinical symptoms were cough (75.5%); chest CT imaging features were mostly linear opacities (42.8%). Pulmonary involvements were more likely to be found in the left lower lung lobe, with a significant difference in the lung total severity score of the individual lung lobes (p < 0.001). Logistic regression analysis revealed age stratification [odds ratio (OR) = 1.92, 95% confidence interval (CI) (1.548-2.383); p < 0.001], prolonged nucleic acid negative conversion time (NCT) (NCT > 8d) [OR = 1.842, 95% CI (1.104-3.073); p = 0.019], and pulmonary diseases [OR = 4.698, 95% CI (1.159-19.048); p = 0.03] as independent OPI risk factors. Asymptomatic and mildly symptomatic patients infected with Omicron had pulmonary involvements which were not uncommon. Potential risk factors for age stratification, prolonged NCT, and pulmonary diseases can help clinicians to identify OPI in asymptomatic and mildly symptomatic patients infected with Omicron.

Efficacy of Opportunistic Screening with Chest CT in Identifying Osteoporosis and Osteopenia in Patients with T2DM.

To explore the validity of the thoracic spine Hounsfield Unit (HU) measured by chest computed tomography (CT) for opportunistic screening of diabetic osteoporosis. The current study attempted to establish a diagnostic threshold for thoracic spine HU in a type 2 diabetes mellitus (T2DM) population with osteoporosis. The current study retrospectively included 334 patients with T2DM. They underwent chest CT and Dual-energy X-ray (DXA) between August 2021 and January 2022 in our hospital. HU values were measured on the resulting chest CT images at thoracic spine 11 and 12 to construct regions of interest. All patients were grouped according to the lowest T-value of DXA examination: osteoporosis, osteopenia and normal bone density. HU values were compared with T-values in each group of patients, and receiver operating characteristics curves were plotted to calculate diagnostic thresholds as well as sensitivity and specificity. There was a strong correlation between the HU values of chest CT and the T-values of DXA (p < 0.01). The sensitivity for osteoporosis was 88.7% for T11 attenuation≤ 98 HU and the specificity for osteoporosis was 87.5% for T12 attenuation ≤ 117HU; the specificity for normal BMD was 85.4% for T11 attenuation ≥ 147 HU and 82% for T12 attenuation ≥ 146 HU. Chest CT can be used to screen patients with T2DM for opportunistic osteoporosis and help determine if they need DXA screening. The current study suggests that when the HU threshold of T11 ≤ 98/T12 ≤ 117, patients may need further osteoporosis screening.

A nomogram model combining computed tomography-based radiomics and Krebs von den Lungen-6 for identifying low-risk rheumatoid arthritis-associated interstitial lung disease.

Quantitatively assess the severity and predict the mortality of interstitial lung disease (ILD) associated with Rheumatoid arthritis (RA) was a challenge for clinicians. This study aimed to construct a radiomics nomogram based on chest computed tomography (CT) imaging by using the ILD-GAP (gender, age, and pulmonary physiology) index system for clinical management. Chest CT images of patients with RA-ILD were retrospectively analyzed and staged using the ILD-GAP index system. The balanced dataset was then divided into training and testing cohorts at a 7:3 ratio. A clinical factor model was created using demographic and serum analysis data, and a radiomics signature was developed from radiomics features extracted from the CT images. Combined with the radiomics signature and independent clinical factors, a nomogram model was established based on the Rad-score and clinical factors. The model capabilities were measured by operating characteristic curves, calibration curves and decision curves analyses. A total of 177 patients were divided into two groups (Group I, n = 107; Group II, n = 63). Krebs von den Lungen-6, and nineteen radiomics features were used to build the nomogram, which showed favorable calibration and discrimination in the training cohort [AUC, 0.948 (95% CI: 0.910-0.986)] and the testing validation cohort [AUC, 0.923 (95% CI: 0.853-0.993)]. Decision curve analysis demonstrated that the nomogram performed well in terms of clinical usefulness. The CT-based radiomics nomogram model achieved favorable efficacy in predicting low-risk RA-ILD patients.