Chest Chest Computed Tomography Research Articles

Chest CT‐based automated vertebral fracture assessment using artificial intelligence and morphologic features

AbstractBackgroundSpinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.PurposeWe present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi‐parametric freeze‐and‐grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.MethodsA chest CT‐based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel‐level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi‐parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior‐posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi‐site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL‐based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.ResultsThe vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image‐level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.ConclusionsOur CT‐based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population‐based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population‐based studies has increased to reduce cumulative radiation exposure.

Factors influencing survival and mortality among adult Aboriginal Australians with bronchiectasis—A 10-year retrospective study

BackgroundThe prevalence of bronchiectasis among adult Aboriginal Australians is higher than that of non-Aboriginal Australians. However, despite evidence to suggest higher prevalence of bronchiectasis among Aboriginal people in Australia, there is sparce evidence in the literature assessing clinical parameters that may predict survival or mortality in this population.MethodsAboriginal Australians residing in the Top End Health Service region of the Northern Territory of Australia aged >18 years with chest computed tomography (CT) confirmed bronchiectasis between 2011 and 2020 were included. Demographics, body mass index (BMI), medical co-morbidities, lung function data, sputum microbiology, chest CT scan results, hospital admissions restricted to respiratory conditions and all-cause mortality were assessed.ResultsA total of 459 patients were included, of whom 146 were recorded deceased (median age at death 59 years). Among the deceased cohort, patients were older (median age 52 vs. 45 years, p = 0.023), had a higher prevalence of chronic obstructive pulmonary disease (91 vs. 79%, p = 0.126), lower lung function parameters (median percentage predicted forced expiratory volume in 1 s 29 vs. 40%, p = 0.149), a significantly greater proportion cultured non-Aspergillus fungi (65 vs. 46%, p = 0.007) and pseudomonas (46 vs. 28%, p = 0.007) on sputum microbiology and demonstrated bilateral involvement on radiology. In multivariate models advancing age, prior pseudomonas culture and Intensive care unit (ICU) visits were associated with increased odds of mortality. Higher BMI, better lung function on spirometry, prior positive sputum microbiology for Haemophilus and use of inhaled long-acting beta antagonist/muscarinic agents may have a favourable effect.ConclusionThe results of this study may be of use to stratify high risk adult Aboriginal patients with bronchiectasis and to develop strategies to prevent future mortality.

Thymic Hyperplasia and COVID-19 Pulmonary Sequelae: A Bicentric CT-Based Follow-Up Study

Thymic Hyperplasia and COVID-19 Pulmonary Sequelae: A Bicentric CT-Based Follow-Up Study

Lung T1 MRI assessments in children with mild cystic fibrosis lung disease.

Lung T1 MRI is a potential method to assess cystic fibrosis (CF)lung disease that is safe, quick, and widely available, but there are no data in children with mild CF lung disease. Assess the ability of lung T1 MRI to detect abnormalities in children with mild CF lung disease. We performed T1 MRI, multiple breath washout (MBW),chest computed tomography (CT),and spirometry in a cohort of 45 children with mild CF lung disease (6-11 years of age). Despite mean normal ppFEV1 values, the majority of children with CF in this study exhibited mild lung disease evident in lung clearance index (LCI)measured by MBW, chest CT Brody scores, and percent normal lung perfusion (%NLP)measured by T1 MRI. The %NLP correlated with chest CT Brody scores, as did LCI, but %NLP and LCI did not correlate with each other. Analysis of the Brody subscores showed that %NLP and LCI largely correlated with different Brody subscores. T1 MRI can detect mild CF lung disease in children and correlates with chest CT findings. The %NLPfrom T1 MRI and LCI correlate with different chest CT Brody subscores, suggesting they provide complementary information about CF lung disease.

Radiographic chest wall abnormalities in primary spontaneous pneumothorax identified by artificial intelligence

Primary spontaneous pneumothorax (PSP) primarily affects slim and tall young males. Exploring the etiological link between chest wall structural characteristics and PSP is crucial for advancing treatment methods. In this case-control study, chest computed tomography (CT) images from patients undergoing thoracic surgery, with or without PSP, were analyzed using Artificial Intelligence. Convolutional Neural Network (CNN) model of EfficientNetB3 and InceptionV3 were used with transfer learning on the Imagenet to compare the images of both groups. A heatmap was created on the chest CT scans to enhance interoperability, and the scale-invariant feature transform (SIFT) was adopted to further compare the image level. A total of 2,312 CT images of 26 non-PSP patients and 1,122 CT images of 26 PSP patients were selected. Chest-wall apex pit (CAP) was found in 25 PSP and three non-PSP patients (p < 0.001). The CNN achieved a testing accuracy of 93.47 % in distinguishing PSP from non-PSP based on chest wall features by identifying the existence of CAP. Heatmap analysis demonstrated CNN's precision in targeting the upper chest wall, accurately identifying CAP without undue influence from similar structures, or inappropriately expanding or minimizing the test area. SIFT results indicated a 10.55 % higher mean similarity within the groups compared to between PSP and non-PSP (p < 0.001). In conclusion, distinctive radiographic chest wall configurations were observed in PSP patients, with CAP potentially serving as an etiological factor linked to PSP. This study accentuates the potential of AI-assisted analysis in refining diagnostic approaches and treatment strategies for PSP.

Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Comparison between high-flow nasal oxygen (HFNO) alternated with non-invasive ventilation (NIV) and HFNO and NIV alone in patients with COVID-19: a retrospective cohort study

BackgroundNon-invasive respiratory support (conventional oxygen therapy [COT], non-invasive ventilation [NIV], high-flow nasal oxygen [HFNO], and NIV alternated with HFNO [NIV + HFNO] may reduce the need for invasive mechanical ventilation (IMV) in patients with COVID-19. The outcome of patients treated non-invasively depends on clinical severity at admission. We assessed the need for IMV according to NIV, HFNO, and NIV + HFNO in patients with COVID-19 according to disease severity and evaluated in-hospital survival rates and hospital and intensive care unit (ICU) lengths of stay.MethodsThis cohort study was conducted using data collected between March 2020 and July 2021. Patients ≥ 18 years admitted to the ICU with a diagnosis of COVID-19 were included. Patients hospitalized for < 3 days, receiving therapy (COT, NIV, HFNO, or NIV + HFNO) for < 48 h, pregnant, and with no primary outcome data were excluded. The COT group was used as reference for multivariate Cox regression model adjustment.ResultsOf 1371 patients screened, 958 were eligible: 692 (72.2%) on COT, 92 (9.6%) on NIV, 31 (3.2%) on HFNO, and 143 (14.9%) on NIV + HFNO. The results for the patients in each group were as follows: median age (interquartile range): NIV (64 [49–79] years), HFNO (62 [55–70] years), NIV + HFNO (62 [48–72] years) (p = 0.615); heart failure: NIV (54.5%), HFNO (36.3%), NIV + HFNO (9%) (p = 0.003); diabetes mellitus: HFNO (17.6%), NIV + HFNO (44.7%) (p = 0.048). > 50% lung damage on chest computed tomography (CT): NIV (13.3%), HFNO (15%), NIV + HFNO (71.6%) (p = 0.038); SpO2/FiO2: NIV (271 [118–365] mmHg), HFNO (317 [254–420] mmHg), NIV + HFNO (229 [102–317] mmHg) (p = 0.001); rate of IMV: NIV (26.1%, p = 0.002), HFNO (22.6%, p = 0.023), NIV + HFNO (46.8%); survival rate: HFNO (83.9%), NIV + HFNO (63.6%) (p = 0.027); ICU length of stay: NIV (8.5 [5–14] days), NIV + HFNO (15 [10–25] days (p < 0.001); hospital length of stay: NIV (13 [10–21] days), NIV + HFNO (20 [15–30] days) (p < 0.001). After adjusting for comorbidities, chest CT score and SpO2/FiO2, the risk of IMV in patients on NIV + HFNO remained high (hazard ratio, 1.88; 95% confidence interval, 1.17–3.04).ConclusionsIn patients with COVID-19, NIV alternating with HFNO was associated with a higher rate of IMV independent of the presence of comorbidities, chest CT score and SpO2/FiO2.Trial registration ClinicalTrials.gov identifier: NCT05579080.

Prognostic value of chest computed tomography in adult inpatients with coronavirus disease 2019 pneumonia in Goma, in the Democratic Republic of the Congo

Objectives: Chest computed tomography (CT) plays a crucial role in evaluating patients with coronavirus disease 2019 (COVID-19) pneumonia. This study aimed to assess the association between initial chest CT findings and mortality in adult inpatients with COVID-19 in the city of Goma, North Kivu province, the Democratic Republic of the Congo. Materials and Methods: This was a multicenter retrospective study of patients hospitalized in Goma with COVID-19 pneumonia who underwent chest CT from January 1, 2021, to December 31, 2022. For each patient included, two experienced radiologists independently reviewed the initial chest CT. A multivariate logistic regression was performed to identify chest CT findings associated with mortality at the 5% significance level. Results: Of 76 patients included in the study, 26 (34.2%) died. The degree of lung parenchymal involvement &gt;25% (adjusted odds ratio [aOR] = 16.27 [3.30–80.16]) and the presence of consolidations (aOR = 3.33 [1.00–11.47]) were predictive of mortality with an area under the receiver operating characteristic curve of 0.8392. Sensitivity was 73.08%, specificity was 82%, positive predictive value was 67.86%, and negative predictive value was 85.42%. Conclusion: Lung involvement &gt;25% and consolidations on chest CT appear to predict death in adult inpatients with COVID-19 pneumonia.

Oligohydramnios affects pulmonary functional/structural abnormalities in school-aged children with bronchopulmonary dysplasia.

The relationship between early life factors and childhood pulmonary function and structure in preterm infants remains unclear. This study investigated the impact of bronchopulmonary dysplasia (BPD) and perinatal factors on childhood pulmonary function and structure. This longitudinal cohort study included preterm participants aged ≥5 years born between 2005 and 2015. The children were grouped by BPD severity according to National Institutes of Health criteria. Pulmonary function tests (PFTs) were performed using spirometry. Chest computed tomography (CT) scans were obtained and scored for hyperaeration or parenchymal lesions. PFT results and chest CT scores were analyzed with perinatal factors. A total 150 children (66 females) aged 7.7 years (6.4-9.9 years) were categorized into non/mild BPD (n=68), moderate BPD (n=39), and severe BPD (n=43) groups. The median z score for forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, and forced midexpiratory flow (FEF25%-75%) were significantly lower in the severe versus non/mild BPD group (-1.24 vs. -0.18, -0.22 vs. 0.41, -1.80 vs. -1.12, and -1.88 vs. -1.00, respectively; all P<0.05). The median z scores of FEV1, FEV1/ FVC, and FEF25%-75% among asymptomatic patients were also significantly lower in the severe versus non/mild BPD group (-0.82 vs. 0.09, -1.68 vs. -0.87, -1.59 vs. -0.61, respectively; all P<0.05). The severe BPD group had a higher median (range) CT score than the non/mild BPD group (6 [0-12] vs. 1 [0-10], P<0.001). Prenatal oligohydramnios was strongly associated with both low pulmonary function (FEV1/FVC<lower limit of normal; odds ratio, 3.54) and high CT score (median difference, 2.54). School-aged children with severe BPD showed airflow limitations and structural abnormalities despite no subjective respiratory symptoms. These results suggest that patients with a history of prenatal oligohydramnios or prolonged mechanical ventilation require extended follow-up.

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function.

Hyperpolarized 129Xe gas MRI is an emerging technique to evaluate and measure regional lung function including pulmonary gas distribution and gas exchange. Chest computed tomography (CT) still remains the clinical gold standard for imaging of the lungs, though, in part due to the rapid CT protocols that acquire high-resolution images in seconds and the widespread availability of CT scanners. Quantitative approaches have enabled the extraction of structural lung parenchymal, airway and vascular measurements from chest CT that have been evaluated in many clinical research studies. Together, CT and 129Xe MRI provide complementary information that can be used to evaluate regional lung structure and function, resulting in new insights into lung health and disease. 129Xe MR-CT image registration can be performed to measure regional lung structure-function to better understand lung disease pathophysiology, and to perform image-guided pulmonary interventions. Here, a method for 129Xe MRI-CT registration is outlined to support implementation in research or clinical settings. Registration methods and applications that have been employed to date in the literature are also summarized, and suggestions are provided for future directions that may further overcome technical challenges related to 129Xe MR-CT image registration and facilitate broader implementation of regional lung structure-function evaluation.

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.

Integration of Artificial Intelligence into Chest Computed Tomography

The integration of Artificial Intelligence (AI) in radiology, especially for chest computed tomography (CT) scan analysis, marked a significant advancement in medical diagnostics, aiming to improve patient care and streamline the workflow for radiologists. This review article examined the role of current AI technologies, including machine learning (ML), deep learning (DL), convolutional neural networks (CNN), and radiomics, in enhancing the detection and characterisation of lung diseases. These technologies are instrumental in identifying complex patterns within imaging data and constructing more informed decisions regarding disease severity, progression, and potential treatment options. Deep learning and CNN have demonstrated effectiveness in analysing the intricate details present in chest CT scans, offering a high degree of accuracy. Radiomics complements these methods by extracting quantitative features from medical images, providing deeper insights into disease characteristics that are not visible through standard imaging techniques. The application of AI has shown promise in improving the diagnosis and management of interstitial lung diseases and lung cancers, contributing to the development of personalised treatment plans. However, this review also highlights limitations, such as small sample sizes in studies, which may impact the generalisability of AI applications in this field. Despite these challenges, the ongoing incorporation of AI into radiological practices is anticipated to significantly enhance the accuracy and efficiency of lung disease diagnostics, setting a foundation for future research and improvements in clinical practice.

Considerations for multidisciplinary management of synchronous primary breast cancer and primary lung cancer - Analysis of thirty-one patients.

The simultaneous (synchronous) presence of primary breast cancer and primary lung cancer diagnosed in a single individual is not an uncommon phenomenon. However, reference data for treatment strategy is scarce and "chaotic". In the present study we discuss the management strategy for this group of patients. We retrospectively reviewed patients in the primary breast cancer database of the Breast Center and the primary lung cancer database of the Thoracic Surgery Department I of Peking University Cancer Hospital. Patients with synchronous primary breast cancer and primary lung cancer who underwent surgery between December 2010 and December 2023 were included in the study. The sequence of outpatient visits, recommendations of multidisciplinary teams, perioperative treatment, and surgical procedures were reviewed. Meanwhile, survival analysis based on propensity score matching with 1:1 ratio was performed between the 31 patients and those with lung cancer only during the same period. A total of 31 patients with synchronous primary breast cancer and primary lung cancer were identified; all of the patients were women. The average age was 61 years. A total of 24 of the patients had visited the breast center first, and routine chest computed tomography (CT) showed evidence of primary lung cancer. The other seven patients had visited the thoracic surgery clinic first, and routine positron emission tomography (PET)-CT revealed the coexistence of primary breast cancer. All the patients had multidisciplinary team consultations, after which 20 patients were recommended to have preoperative treatment for breast cancer, two patients were recommended to have preoperative treatment for lung cancer, and nine patients were recommended to undergo surgery directly. After surgery, 23 patients received postoperative adjuvant treatment for breast cancer, and no patients needed postoperative adjuvant treatment for lung cancer. Survival analysis showed that there was no significant difference between the 31 patients and those with lung cancer only. Routine chest CT is needed for breast cancer patients before surgery, and PET-CT is required for the accurate staging of lung cancer patients. A multidisciplinary expert team should manage synchronous primary breast cancer and primary lung cancer. Emphasis should be placed on patients who need preoperative treatment before surgery. Particularly, for patients who need preoperative chemotherapy, a regimen should be chosen that balances the treatment of lung cancer and breast cancer.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Longitudinal long term follow up investigation on the carcinogenic impact of polyhexamethylene guanidine phosphate in rat models

Polyhexamethylene guanidine phosphate (PHMG-p) is a major component in humidifier disinfectants, which cause life-threatening lung injuries. However, to our knowledge, no published studies have investigated associations between PHMG-p dose and lung damage severity with long-term follow-up. Therefore, we evaluated longitudinal dose-dependent changes in lung injuries using repeated chest computed tomography (CT). Rats were exposed to low (0.2 mg/kg, n = 10), intermediate (1.0 mg/kg, n = 10), and high (5.0 mg/kg, n = 10) doses of PHMG-p. All rats underwent repeated CT scans after 10 and 40 weeks following the first exposure. All CT images were quantitatively analyzed using commercial software. Inflammation/fibrosis and tumor counts underwent histopathological evaluation. In both radiological and histopathologic results, the lung damage severity increased as the PHMG-p dose increased. Moreover, the number, size, and malignancy of the lung tumors increased as the dose increased. Bronchiolar–alveolar hyperplasia developed in all groups. During follow-up, there was intergroup variation in bronchiolar–alveolar hyperplasia progression, although bronchiolar–alveolar adenomas or carcinomas usually increase in size over time. Thirty-three carcinomas were detected in the high-dose group in two rats. Overall, lung damage from PHMG-p and the number and malignancy of lung tumors were shown to be dose-dependent in a rat model using repeated chest CT scans during a long-term follow-up.

Latent class analysis of chest CT abnormalities to define subphenotypes in patients with MPO-ANCA-positive microscopic polyangiitis

BackgroundPatients with microscopic polyangiitis (MPA) and positive myeloperoxidase antineutrophil cytoplasmic antibody (MPO-ANCA) may present with various abnormalities in chest computed tomography (CT). This study aimed to identify subphenotypes using latent class analysis (LCA) and to explore the relationship between the subphenotypes and clinical patterns, as well as compare the clinical characteristics of these subphenotypes in patients with MPO-ANCA-positive MPA (MPO-MPA). MethodsThe study identified subphenotypes using LCA based on chest CT findings in 178 patients with MPO-MPA and pulmonary involvement from June 2014 to August 2022. ResultsLCA identified 27 participants (15.2%) in class 1, 43 (24.1%) in class 2, 35 (19.7%) in class 3, and 73 (41.0%) in class 4. Class 1 was characterized by prominent inflammatory exudation, class 2 by fibrosis and architectural distortion, class 3 by predominantly bronchiectasis, and class 4 by lesions mixed with inflammation and fibrosis. Class 1 had the highest level of extrapulmonary disease activity, with 77.8% of patients experiencing diffuse alveolar hemorrhage. Class 2 had the lowest level of extrapulmonary disease activity, with 41.9% of patients showing usual interstitial pneumonia. Class 3 patients were more likely to have complications involving the ear, nose, and throat, as well as pulmonary infections before treatment, and they exhibited the best outcomes. The characteristics and outcomes of class 4 were intermediate among the four classes. ConclusionsThese findings suggest that bronchiectasis may represent a unique pattern of pulmonary involvement in MPO-MPA, highlighting the importance of screening for bronchiectasis in MPO-MPA and identifying optimal management strategies.

Significance of lung nodules detected on chest CT among adult Aboriginal Australians - a retrospective descriptive study.

There are limited data on chest computed tomography (CT) findings in the assessment of lung nodules among adult Aboriginal Australians. In this retrospective study, we assessed lung nodules among a group of adult Aboriginal Australians in the Northern Territory of Australia. Patients who underwent at least two chest CT scans between 2012 and 2020 among those referred to undergo lung function testing (spirometry) were included. Chest CT scans were assessed for the number, location, size and morphological characteristics of lung nodules. Of the 402 chest CTs assessed, 75 patients (18.7%) had lung nodules, and 57 patients were included in the final analysis with at least two CT scans available for assessment over a median follow-up of 87 weeks. Most patients (68%) were women, with a median age of 58 years and smoking history in 83%. The majority recorded only a single nodule 43 (74%). Six patients (10%) were diagnosed with malignancy, five with primary lung cancer and one with metastatic thyroid cancer. Of the 51 (90%) patients assessed to be benign, 64 nodules were identified, of which 25 (39%) resolved, 38 (59%) remained stable and one (1.8%) enlarged on follow-up. Nodules among patients with malignancy were typically initially larger and enlarged over time, had spiculated margins and were solid, showing no specific lobar predilection. Most lung nodules in Aboriginal Australians are likely to be benign. However, a proportion could be malignant. Further prospective studies are required for prognostication and monitoring of lung nodules in this population.

COVID-19 Chest Manifestation on CT Scan and Associated Risk Factors for Developing Pulmonary Fibrosis.

This retrospective study describes the imaging findings on chest computed tomography (CT) scans of coronavirus disease 2019 (COVID-19) patients as well as the prevalence of pulmonary fibrosis and the potential risk factors for the disease. One of the major COVID-19 centers in the western province of Saudi Arabia,the King Abdullah Medical Complex in Jeddah, was the site of this study. All adult COVID-19 patients who got a CT chest scan between January 2020 and April 2022 were included in the trial. The imaging findings and pulmonary severity scores (PSS) were obtained from the patients' CT chest. Patients were divided into two groups according to the evidence of fibrotic-like lung changes; clinical and radiological data between the two groups were subsequently compared. Data from the patients' electronic records was collected. The average patient age was 56.4 years, and most (73.5%) patients were men. Two-thirds of the patients had comorbidities (69.1%). CT scans revealed that diffuse lung infiltration is reported in 61% of cases, followed by lower lobes in 19.9%. Ground glass opacity (94.1%), consolidation (76.5%), septal thickening, and/or reticulation (24.4%) were the main chest findings during the initial CT scan. Fibrotic-like lung changes were developed in 9.6% of patients. Patients known to have a positive history of hypertension (p-value = 0.031) and coronary artery disease (CAD) (p-value = 0.011) were found to be significantly more likely to develop lung fibrosis. The patients' pneumonia severity score was significantly higher among the lung fibrotic patients (p-value = 0.026). Also, patients who were diagnosed with pulmonary fibrosis stayed longer in the hospital (p-value 0.001). Sex and age did not correlate significantly with risk of lung fibrosis. Pulmonary fibrosis was observed in 9.6% of COVID-19 patients. A close follow-up of patients with severe pneumonia, prolonged hospitalization, and pre-existing CAD and hypertension was necessary, as pulmonary fibrosis was more likely to occur as a result of these factors. There is a need for a thorough, long-term investigation with a large sample size.

Diagnostic Strategy for Suspected Unilateral Absence of the Pulmonary Artery.

To analyze the diagnostic strategy applied to seven patients with UAPA who were examined and subsequently treated at the National Lung Hospital, Hanoi, Vietnam. All seven patients, including three pediatric cases (1, 2, and 14 years old) and four adult cases (21, 26, 44, and 53 years old), had a history of recurrent pneumonia, and the clinical symptoms on admission included cough, progressive dyspnea, chest pain, and fatigue. The patients were initially examined clinically, followed by hematological testing, blood biochemistry testing, and chest X-ray radiology. The results suggested UAPA, so echocardiography and contrast-enhanced chest computed tomography (CT) were performed as soon as practical. The echocardiographic and CT imaging findings confirmed the suspected diagnosis of UAPA in all seven patients, which was accompanied by congenital heart disease in three patients. Three of the seven patients had mild and medium pulmonary hypertension. All seven patients were treated with drugs, which led to improvement in symptoms. Frontal chest X-ray provided the initial signs suggesting a diagnosis of UAPA. Subsequent echocardiography and contrast-enhanced chest CT were effective diagnostic tools for fast and accurate confirmation of UAPA.</p>.

Relationship of computed tomography-based measurements with symptom perception and quality of life in patients with severe asthma

BackgroundSymptom perception and quality of life (QOL) are important domains for properly managing severe asthma. This study aimed to assess the relationship between airway structural and parenchymal variables measured using chest computed tomography (CT) and subjective symptom perception and QOL in patients with severe asthma enrolled in the Korean Severe Asthma Registry. MethodsThis study used CT-based objective measurements, including airway wall thickness (WT), hydraulic diameter, functional small airway disease (fSAD), and emphysematous lung (Emph), to assess their association with subjective symptom (cough, dyspnea, wheezing, and sputum) perception measured using the visual analog scale, and QOL measured by the Severe Asthma Questionnaire (SAQ). ResultsA total of 94 patients with severe asthma were enrolled in this study. The WT and fSAD% were significantly positively associated with cough and dyspnea, respectively. For QOL, WT and Emph% showed significant negative associations with the SAQ. However, there was no significant association between lung function and symptom perception or between lung function and QOL. ConclusionOverall, WT, fSAD%, and Emph% measured using chest CT were associated with subjective symptom perception and QOL in patients with severe asthma. This study provides a basis for clarifying the clinical correlates of imaging-derived metrics and for understanding the mechanisms of respiratory symptom perception.