Chest Computed Tomography Images Research Articles

Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts. Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n=137; internal validation cohort: n=59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTLmodelsincluding Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis. The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts. The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.

FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images.

Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample sizes and a lack of standardization in data preprocessing and evaluation metrics. This study presents a comparative analysis of four vision transformers regarding their efficacy in accurately detecting and classifying patients with Pulmonary Fibrosis and their ability to localize abnormalities within Images obtained from Computerized Tomography (CT) scans. The dataset consisted of 13,486 samples selected out of 24647 from the Pulmonary Fibrosis dataset, which included both PF-positive CT and normal images that underwent preprocessing. The preprocessed images were divided into three sets: the training set, which accounted for 80% of the total pictures; the validation set, which comprised 10%; and the test set, which also consisted of 10%. The vision transformer models, including ViT, MobileViT2, ViTMSN, and BEiT were subjected to training and validation procedures, during which hyperparameters like the learning rate and batch size were fine-tuned. The overall performance of the optimized architectures has been assessed using various performance metrics to showcase the consistent performance of the fine-tuned model. Regarding performance, ViT has shown superior performance in validation and testing accuracy and loss minimization, specifically for CT images when trained at a single epoch with a tuned learning rate of 0.0001. The results were as follows: validation accuracy of 99.85%, testing accuracy of 100%, training loss of 0.0075, and validation loss of 0.0047. The experimental evaluation of the independently collected data gives empirical evidence that the optimized Vision Transformer (ViT) architecture exhibited superior performance compared to all other optimized architectures. It achieved a flawless score of 1.0 in various standard performance metrics, including Sensitivity, Specificity, Accuracy, F1-score, Precision, Recall, Mathew Correlation Coefficient (MCC), Precision-Recall Area under the Curve (AUC PR), Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). Therefore, the optimized Vision Transformer (ViT) functions as a reliable diagnostic tool for the automated categorization of individuals with pulmonary fibrosis (PF) using chest computed tomography (CT) scans.

Abstract 12380: Importance of Quantifying Lung Fluid Status in Patients With Heart Failure Using Chest Computed Tomography

Background: Heart failure (HF) is a complex condition that requires accurate assessment for optimal management. B-type natriuretic peptide (BNP) has been widely used for diagnosis and monitoring, but its correlation with symptoms and prognosis can be inconsistent. This study investigates the utility of chest computed tomography (CT)-based evaluation of lung congestion as an additional tool in HF. Methods: CT imaging (SOMATOM Force, Siemens Healthineers) was performed in 129 HF patients on admission. Concurrently, BNP levels were measured. For image analysis, mean lung density (MLD), an index of lung congestion obtained from imaging, was analyzed by image analysis software (Syngo. CT Pulmo 3D, Siemens Healthineers). MLD was converted to fluid content (FC) in percentage units according to the equation: FC [%] = (MLD + 1000) / 10. The correlation between CT findings, BNP levels, clinical data, and prognosis was analyzed. Results: The FC of HF patients were significantly higher than those of non-HF patients (30.2 ± 6.2% vs. 18.2 ± 4.1%, p &lt; 0.01), and showed a weak correlation with BNP levels at admission (r = 0.205, p = 0.020). The subjects were divided into four groups based on FC values (median 30.2%) and BNP levels (median 617 pg/mL): S1 (&lt;30.2, &lt;617), 2 (≥30.2, &lt;617), S3 (&lt;30.2, ≥617), and S4 (≥30.2, ≥617). During the observation period of 7 to 38 months (mean 19.5 months), 38 out of 129 patients died. FC values were significantly higher in patients who were readmitted (32.2 ± 6.2% vs. 29.4 ± 6.1%, p = 0.024). Cardiovascular death and survival were compared between groups (Figure). Group S1 showed significantly better survival compared to the other groups. Higher mortality rates were observed in groups with the higher FC values, even in those with lower BNP levels. Conclusion: The present data suggest that the evaluation of the lung congestion level (FC value) using CT imaging on admission in HF patients is an additional indicator for accurate prognostic evaluation, in addition to BNP.

Abstract 12452: Risk Stratification in Cardiogenic Shock Patients With Venoarterial Extracorporeal Membrane Oxygenation Using Lung Computed Tomography Density and Model for Endstage Liver Disease Score

Background: End-organ dysfunction often occur in patients receiving femoro-femoral veno-arterial extracorporeal membrane oxygenation (VA ECMO) and it has been reported to be associated with poor prognosis. This study aimed to elucidate the ability to risk stratification in patients with cardiogenic shock (CS) who received VA ECMO using lung computed tomography (CT) density and Model for End-stage Liver Disease exclude INR (MELD-XI) score. Methods: This is a single-center retrospective observational study targeted 65 CS patients who has had available chest CT images on VA ECMO support followed by MCS escalation from 2012 to 2021. The average density of lung CT images in the lung field at the level of tracheal bifurcation was measured using region-of-interest methods. MELD-XI scores were also calculated. The primary endpoint was 180-day all cause death after MCS escalation. Results: Twenty-two patients (34%) died within 180 days during the follow-up period [659 (78-1,544) days]. According to the Cox regression analysis higher MELD-XI scores (≥ 18) (HR; 2.68; 95% CI, 1.05-6.86; p=0.039), and higher lung CT density [≥ -481 Hounsfield unit (HU)] (HR, 5.64; 95% CI, 1.95-16.28; p=0.001) were independently associated with all cause death. Moreover, combination of both lung CT density and MELD-XI score further refine risk stratification in current patient population : group A, high lung CT density (≥-481 HU) and high MELD-XI score (≥18); group B, high lung CT density (≥-481 HU) or high MELD-XI score (≥18); and group C, low lung CT density (&lt;-481 HU) and low MELD-XI score (&lt;18) (group A:31.3% vs. group B:65.2% vs. group C:88.5%, log-rank p&lt;0.001). Conclusions: Combination of higher lung CT density and higher MELD-XI score is a useful predictor of prognosis in patients with VA ECMO requiring escalation of MCS.

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.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

Chest CT Image based Lung Disease Classification - A Review.

Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis. Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with the awareness they require in order to recognize and classify certain medical disorders.

COV-CT-NET - A DEEP LEARNING MODEL FOR COVID-19, COMMUNITY-ACQUIRED PNEUMONIA DETECTION USING CT IMAGES

The world has witnessed the deadly impact of the Novel Corona Virus (COVID-19), claiming millions of lives since its outbreak in early December 2019. Early virus detection plays a crucial role in controlling this highly contagious disease. Though Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the current standard for confirmation of COVID-19, it is time-consuming. Computed Tomography (CT) imaging of the lungs can preferably be used for fast diagnosis of the disease as it is more sensitive and can detect complications. Due to the unavailability of adequate expertise, a deep learning-based model on CT images is a potential solution for fast detecting SARS Cov2 virus. In this study, we developed a simple but robust Convolution Neural Network model with multiclass detection ability between normal lungs, COVID-19 infected lungs and any other Community-Acquired Pneumonia (CAP) infection using Chest CT images. It is tested on a publicly available dataset, COVID-CT-MD and it achieved slice level accuracy of 99% on test dataset. We also attempted slice-level prediction of the unlabelled slices available in the dataset of COVID-19 and CAP cases.

Assessment of Patients Radiation Doses during Computed Tomography Chest Imaging Examination: Propose Diagnostic Reference Level

Wide variation in mean doses are noted across the region. Therefore, the chest CT examination practice within the region is optimized

Inter-rater reliability of a novel objective endpoint for benign central airway stenosis interventions: Segmentation-based volume rendering of computed tomography scans.

To evaluate the reliability of a novel segmentation-based volume rendering approach for quantification of benign central airway obstruction (BCAO). A retrospective single-center cohort study. Data were ascertained using electronic health records at a tertiary academic medical center in the United States. Patients with airway stenosis located within the trachea on two-dimensional (2D) computed tomography (CT) imaging and documentation of suspected benign etiology were included. Four readers with varying expertise in quantifying tracheal stenosis severity were selected to manually segment each CT using a volume rendering approach with the available free tools in the medical imaging viewing software OsiriX (Bernex, Switzerland). Three expert thoracic radiologists were recruited to quantify the same CTs using traditional subjective methods on a continuous and categorical scale. The interrater reliability for continuous variables was calculated by the intraclass correlation coefficient (ICC) using a two-way mixed model with 95% confidence intervals (CI). Thirty-eight patients met the inclusion criteria, and fifty CT scans were selected for measurement. The most common etiology of BCAO was iatrogenic in 22 patients (58%). There was an even distribution of chest and neck CT imaging within our cohort. The average ICC across all four readers for the volume rendering approach was 0.88 (95% CI, 0.84 to 0.93), suggesting good to excellent agreement. The average ICC for thoracic radiologists for subjective methods on the continuous scale was 0.38 (95% CI, 0.20 to 0.55), suggesting poor to fair agreement. The kappa for the categorical approach was 0.26, suggesting a slight to fair agreement amongst the raters. In this retrospective cohort study, agreement was good to excellent for raters with varying expertise in airway cross-sectional imaging using a novel segmentation-based volume rendering approach to quantify BCAO. This proposed measurement outperformed our expert thoracic radiologists using conventional subjective grading methods.

Anterior mediastinal nodular lesion segmentation from chest computed tomography imaging using UNet based neural network with attention mechanisms

Anterior mediastinal nodular lesion segmentation from chest computed tomography imaging using UNet based neural network with attention mechanisms

An organ-effective modulation for non-contrast chest computed tomography imaging: effect on image quality and thyroid exposure reduction.

We investigate the efficacy of organ-effective modulation (OEM) technique for thyroid dose reduction among various body habitus and its impact on image quality in chest non-contrast computed tomography (CT). We prospectively enrolled 64 patients who underwent non-contrast chest CT from January to May 2022. The skin-absorbed radiation dose over the thyroid (Dthyroid) was obtained using a thermoluminescence dosemeter. Signal-to-noise ratio and image noise was also quantitatively assessed. In subjective analyses, two radiologists independently evaluated images based on a 5-point scale. The OEM group showed a markedly decrease in Dthyroid when compared with the non-OEM group (p<0.05). No significant difference was observed regarding the image noise (p<0.05), except for the ventral air space. The subjective scores of two radiologists showed no significant differences between the non-OEM and OEM groups. OEM can effectively reduce the radiation exposure of thyroid without compromising on image quality in non-contrast chest CT.

Prevalence and anatomical characteristics of medial-basal segment in right lung.

The anatomic features and surgical techniques focusing on the right medial-basal segment (RS7) are few reported. This study aimed to accurately define the new nomenclature and classifications of B7 anatomy, elucidate its prevalence and anatomical characteristics and summarize the surgical outcomes. Between August 2019 and February 2022, 5023 patients were admitted for pulmonary nodules. Their chest computed tomography images were obtained. All of the images were reconstructed in 3 dimensions. The RS7 were screened according to their new definition and statistically analysed for their anatomical characteristics. The bronchovascular anatomy of S7 can be newly classified into 6 types: B7a type, B7p type, B7o type, B7t type, BX7a type and BX7t type. The B7 anterior to the inferior pulmonary vein (IPV) was B7a (a, anterior) (3617/5023, 72%). The B7 posterior to IPV was B7p (p, posterior) (306/5023, 6.1%). The B7 over IPV was B7o (o, over) (904/5023, 18%). The B7 through IPV was B7t (t, through) (7/5023, 0.14%). An abnormal origin of B7 was named the BX7 type. The BX7 anterior to IPV sharing a common trunk with B8 was named BX7a (a, anterior) (176/5023, 3.5%). The BX7 through IPV originated from B10 and was named BX7t (t, through) (13/5023, 0.26%). 0.2% (12/5023) of patients had the nodules in RS7 and underwent RS7 surgery. The variation pattern of B7 is far more complex than expected. The results of this study can help surgeons better understand S7 and perform segmentectomies more accurately.

Conditional cascaded network (CCN) approach for diagnosis of COVID-19 in chest X-ray and CT images using transfer learning

The COVID-19 pandemic has caused substantial global health and economic damage, with over five million confirmed cases worldwide. The importance of the rapid, accurate diagnosis of infected patients has been underscored. However, due to the shortage of testing kits and the time-consuming, troublesome nature of the manual RT-PCR test, an automated, efficient diagnosis system using chest medical images for the early screening of COVID-19 is crucial. In this paper, an automated approach for the rapid, accurate diagnosis of COVID-19 in chest X-ray and computed tomography (CT) images using transfer learning is presented. The proposed technique leverages the conditional cascaded network approach, which employs multiple levels of networks to analyze images with high confidence. Transfer learning is employed with seven commonly used existing convolutional neural network architectures and four datasets for X-ray and CT images. Various regularization, optimization, dropout, and data augmentation techniques are examined through a series of experiments with three optimizers. Our technique is compared with other state-of-the-art techniques, and the achieved results demonstrate highly promising performance metrics. Additionally, occlusion specificity and gradient-weighted class activation mapping techniques are employed to understand the network output better. The proposed technique is highly adaptable and scalable and does not require manual hyperparameter tuning.

Application value of a computer-aided diagnosis and management system for the detection of lung nodules.

Computer-aided diagnosis (CAD) systems can help reduce radiologists' workload. This study assessed the value of a CAD system for the detection of lung nodules on chest computed tomography (CT) images. The study retrospectively analyzed the CT images of patients who underwent routine health checkups between August 2019 and November 2019 at 3 hospitals in China. All images were first assessed by 2 radiologists manually in a blinded manner, which was followed by assessment with the CAD system. The location and classification of the lung nodules were determined. The final diagnosis was made by a panel of experts, including 2 associate chief radiologists and 1 chief radiologist at the radiology department. The sensitivity for nodule detection and false-positive nodules per case were calculated. A total of 1,002 CT images were included in the study, and the process was completed for 999 images. The sensitivity of the CAD system and manual detection was 90.19% and 49.88% (P<0.001), respectively. Similar sensitivity was observed between manual detection and the CAD system in lung nodules >15 mm (P=0.08). The false-positive nodules per case for the CAD system were 0.30±0.84 and those for manual detection were 0.24±0.68 (P=0.12). The sensitivity of the CAD system was higher than that of the radiologists, but the increase in the false-positive rate was only slight. In addition to reducing the workload for medical professionals, a CAD system developed using a deep-learning model was highly effective and accurate in detecting lung nodules and did not demonstrate a meaningfully higher the false-positive rate.

Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification

The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel principal component analysis (PCA). The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.

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.

Can lung airway geometry be used to predict autism? A preliminary machine learning-based study.

The goal of this study is to assess the feasibility of airway geometry as a biomarker for autism spectrum disorder (ASD). Chest computed tomography images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. Fifty-four scans were obtained for analysis, including 31 ASD cases and 23 controls. A feature selection and classification procedure using principal component analysis and support vector machine achieved a peak cross validation accuracy of nearly 89% using a feature set of eight airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branching angles between children with ASD and the control population.

Factors associated with changes in activities of daily living from admission to discharge among older patients with pneumonia

IntroductionIt has not been fully elucidated that nutritional parameters affect the change of activities of daily living (ADL) during pneumonia treatment. This study assessed the impact of nutritional status, including erector spinae muscle (ESM) size on ADL changes from admission to discharge among older patients with community-acquired pneumonia (CAP). MethodsWe retrospectively included patients (age: ≥65 years) who were admitted to the hospital for CAP and underwent chest computed tomography (CT) on admission. ADL was evaluated using the Barthel index, and patients were divided into the maintained or improved ADL group and the declined ADL group from admission through discharge. The ESM cross-sectional area was measured on a single-slice CT image. Logistic regression models were applied for assessing factors associated with changes in ADL. ResultsA total of 523 patients hospitalized for CAP (median age 86 years) were evaluated. The declined group had significantly higher ADL levels on admission, a greater frequency of smoking history and malignancy, and a lower frequency of cerebrovascular disease and dementia. No significant difference in ESM size was observed between the groups. Multivariate analysis revealed that higher ADL levels on admission (odds ratio 1.034, interquartile range 1.026–1.043) and malignancy (3.002, 1.150–7.836) were associated with a decline in ADL, whereas cerebrovascular disease (0.579, 0.373–0.900) was related to improvement or maintenance of ADL. ConclusionsAlthough nutritional status might not affect the change of ADL among older patients hospitalized with pneumonia, a cerebrovascular disease history may be a good predictor for ADL improvement.