Chest Computed Tomography Scan Research Articles

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on follow-up CT imaging.

Different pathological stages of lung adenocarcinoma require different surgical strategies and have varying prognoses. Predicting their invasiveness is clinically important. This study aims to develop a nomogram to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) based on follow-up computed tomography (CT) imaging. We retrospectively collected data of 623 GGNs from 601 patients who underwent two follow-up chest CT scans and were confirmed as lung adenocarcinoma by postoperative pathology between June 2017 and August 2023. These patients were randomly divided into training and testing sets in a 7:3 ratio. Eighty-seven GGNs from 86 patients who underwent surgery between September 2023 and April 2024 were prospectively collected as a validation set. The volume, mean density, solid component volume (SV), percentage of solid component (PSC), and mass of GGNs were evaluated using the InferRead CT Lung software. Patients were classified into Group A (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and Group B (invasive adenocarcinoma). Three predictive models were established: model 1 utilized clinical characteristics and morphological features on pre-surgical CT, model 2 incorporated clinical characteristics, morphological features and quantitative parameters on pre-surgical CT, and model 3 utilized all selected features on baseline and pre-surgical CT. Model 3 achieved a satisfying area under the curves values of 0.911, 0.893, and 0.932 in the training, testing, and validation sets, respectively, demonstrating superior predictive performance than model1 (0.855, 0.858, and 0.816) and model2 (0.895, 0.891, and 0.903). A nomogram was constructed based on model 3. Calibration curves showed a good fit, and decision curve analysis showed that the nomogram was clinically useful. The nomogram based on morphological features and quantitative parameters from follow-up CT images showed good discrimination and calibration abilities in predicting the invasiveness of lung adenocarcinoma manifesting as GGNs.

Right Lung Agenesis Associated with Dextrocardia in Adulthood

Introduction: Pulmonary agenesis is a rare lung disease, occurring in approximately 1 out of every 100,000 births. Most cases of pulmonary agenesis result in death during the neonatal period. Although survival in cases of pulmonary agenesis is rare, it is possible to encounter lung agenesis in adults. Case: A 26-year-old female patient presented with progressive dyspnea. On physical examination, retraction of the breathing muscles, reduced chest excursions, and decreased breath sounds on the right side were observed. A chest X-ray raised suspicion of pulmonary agenesis. A computed tomography (CT) scan showed hypoplasia of the right lung, accompanied by mediastinal shift, hyperinflation of the left lung, and retraction of the diaphragm and liver to the right. Spirometry showed moderate obstruction. Conclusion: Pulmonary agenesis in adult patients is extremely rare. Chest X-rays and CT scans are the main diagnostic modalities for pulmonary agenesis. There is no specific treatment for asymptomatic cases. Management focuses on conservative and symptomatic care.

Simultaneous unilateral thoracoscopic resection of bilateral pulmonary sequestration

BackgroundIntra-lobar (ILS) and extra-lobar lung (ELS) sequestrations represent rare congenital lung malformations. Despite their benign nature, the lesions pose risks such as recurrent pulmonary infections, hemoptysis, congestive heart failure, and tumor development. Pulmonary sequestration (PS) typically manifests in two forms, ILS and ELS, with bilateral occurrence being exceptionally rare and mostly requiring bilateral thoracic surgery.Case presentationA 9-year-old child, who initially presented with bilateral lung lesions without respiratory symptoms, was diagnosed with PS following a chest computed tomography scan. The surgical approach was determined based on the absence of inflammation and the clear demarcation of the lesions from normal lung tissue, highlighted by a unique tissue connection between the ILS and ELS across the chest cavities. We used a novel method wherein the left ELS was successfully pulled into the right chest cavity and both sequestrations were concurrently resected. Postoperative recovery was smooth, with no complications or residual lesions.ConclusionsOur findings highlight the importance of thorough preoperative planning with enhanced computed tomography. Simultaneous unilateral thoracoscopic surgery can be a viable, less invasive option for treating bilateral PS, offering benefits such as reduced recovery time and better cosmetic outcomes.

Development of Artificial Intelligence-Based Programs for the Diagnosis of Myocarditis in COVID-19 Using Chest Computed Tomography Data»

It has been established that 7.2% of patients hospitalized with coronavirus disease (COVID-19) exhibit signs of heart disease, with 23% of these patients experiencing heart failure. Currently, there is a lack of data on chest computed tomography (CT) for diagnosing myocarditis associated with COVID-19. The aim. To justify the feasibility and develop classification models for diagnosing myocarditis in COVID-19 patients based on chest CT data processing. Materials and methods. A retrospective analysis of data from 140 COVID-19 patients was conducted. Chest CT scans were analyzed using DRAGONFLY software, with permission from Object Research Systems. The COVID-CT-MD database, which includes CT data from 169 confirmed cases of SARS-CoV-2 infection, was used to build classification models. The regions of interest were fragments of heart CT images. Texture analysis methods were employed to create diagnostic models. Results. It was shown that the average density of the myocardium of a patient with a confirmed diagnosis of SARS-CoV-2 infection according to the Hounsfield scale does not essentially differ from the densitometric indicators of a healthy person. Therefore, the research was focused on finding structural changes in CT images for their use in constructing diagnostic models. The use of different classification algorithms had little effect on classification accuracy, probably due to the informational content of the input data. However, the obtained accuracy of the diagnostic models is acceptable and allows them to be used to support medical decision-making regarding diagnosis and treatment. Conclusions. Using classic methods, myocarditis was diagnosed in 7.1% of patients with severe pneumonia caused by the coronavirus. The global data closely aligns with the results of our clinical studies. The obtained results allowed for assessing structural changes in the myocardium characteristic of the acute form of SARS-CoV-2 infection. The constructed classification models indicate that specific changes in the myocardium during the acute form of SARS-CoV-2 infection can be identified using CT. The highest diagnostic accuracy on test samples reached 74%. The implementation of the developed diagnostic programs based on texture analysis of CT data and artificial intelligence technologies enables the diagnosis of myocarditis and the assessment of long-term treatment efficiency. Creation of these diagnostic programs using artificial intelligence technologies significantly simplifies the work of radiologists and improves the efficiency of myocarditis diagnosis in patients with SARS-CoV-2 infection.

Comparison and Analysis of Clinical Characteristics of Common COVID-19 and NSIP Patients

Objective: To investigate the relationship between coronavirus disease 2019 (COVID-19) and non-specific interstitial pneumonia (NSIP), with a focus on the clinical features of COVID-19 and NSIP, and the key points of differential diagnosis. Methods: The clinical data of 20 patients with common-type COVID-19 and NSIP admitted to Linyi People’s Hospital from January 21, 2020, to June 21, 2022, were retrospectively analyzed. Gender, age, history of residence in Hubei province, underlying diseases, clinical manifestations, laboratory test results (including blood routine indexes, inflammatory markers, liver function indexes, and coagulation indexes), and computed tomography (CT) scan images were compared between the two groups. Results: COVID-19 patients were younger than NSIP patients (P < 0.05). Nine COVID-19 patients had a travel history to Hubei province, while none of the NSIP patients did (P < 0.05). Eight COVID-19 patients had underlying chronic conditions, fewer than the NSIP group (12 patients; P < 0.05). Both groups experienced symptoms such as shortness of breath, expectoration, fatigue, and runny nose, but fever and cough were more severe and more frequent in the COVID-19 group. Compared to normal reference ranges, both groups exhibited normal white blood cell counts (WBC) and liver function indexes, but elevated lymphocyte counts (LYMP), inflammatory markers, and coagulation indexes, with reduced neutrophil counts (NE). WBC and LYMP were higher in the COVID-19 group compared to the NSIP group. Male patients in the COVID-19 group had higher erythrocyte sedimentation rates and C-reactive protein values than those in the NSIP group, while procalcitonin levels were lower in the COVID-19 group, although the differences were not statistically significant (all P > 0.05). The NE count in the COVID-19 group was significantly lower than in the NSIP group (P < 0.05). Alanine aminotransferase, total bilirubin, and indirect bilirubin were significantly higher in the COVID-19 group compared to the NSIP group (P < 0.05). Chest CT scans of both groups showed bilateral patchy ground-glass opacities, but the lesions in COVID-19 patients were scattered. NSIP patients’ chest CTs showed diffuse lesions centered around the hilum or multiple lesions in both lungs, with pleural involvement being rare. Conclusion: While there are certain specific clinical, laboratory, and imaging findings in both COVID-19 and NSIP, the specificity of these features is not high. Differentiating the two requires careful consideration of epidemiological history, nucleic acid testing, and antigen-antibody levels.

Artificial intelligence-powered chest computed tomography analysis unveils prognostic insights for COVID-19 mortality among prevalent hemodialysis patients.

Coronavirus disease 2019 (COVID-19) has led to severe pneumonia and mortality worldwide, however, clinical outcomes in end-stage renal disease patients remain unclear. This study evaluates the prognostic value of chest computed tomography (CT) findings in predicting COVID-19-related outcomes in prevalent hemodialysis patients. We retrospectively analyzed 326 prevalent hemodialysis patients diagnosed with COVID-19 who underwent chest CT scans. Characteristics assessed included pleural effusion, lung involvement volume, nodular consolidation, patchy infiltration, and ground-glass opacity. Artificial intelligence (AI)-assisted CT analysis quantified lung involvement. The primary endpoint was in-hospital mortality. Clinical data were collected, and logistic regression analysis assessed the association between CT findings and mortality. The mean age of the patients was 66.7 ± 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement >10%, 32.5% had pleural effusion, 68.7% had nodular consolidation, 57.1% had patchy infiltration, and 58.0% had ground-glass opacity. Seventy patients (21.5%) died. Multivariate logistic regression analysis identified lung involvement >2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35-65.63), pleural effusion (OR, 3.28; 95% CI, 1.15-9.35), nodular consolidation (OR, 4.08; 95% CI, 1.12-14.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17-12.03) as significant mortality risk factors. Chest CT findings, including lung involvement >2.7% and the presence of pleural effusion, nodular consolidation, and patchy infiltrates, significantly indicated mortality in COVID-19 pneumonia among prevalent hemodialysis patients. AI-assisted CT analysis proved useful in assessing lung involvement extent, showing that even minimal lung involvement can be associated with increased mortality.

Evaluating the effectiveness of artificial intelligence imaging in the qualitative diagnosis of pulmonary nodules

Our study aimed to evaluate the effectiveness of artificial intelligence (AI) image diagnostic systems in the qualitative diagnosis of pulmonary nodules. We analyzed 291 cases from June 2023 to January 2024 at Chongqing University Three Gorges Hospital. All patients in the study underwent low-dose chest computed tomography scans, which identified lung nodules, followed by thoracic surgery for pathological confirmation. We compared the predictive accuracy of AI-based diagnosis with that of physician-based diagnosis in distinguishing between benign and malignant lung nodules. Among the 291 lung nodules examined, 226 were cancerous, and 65 were benign. Receiver operating characteristic (ROC) curves, plotted based on the malignancy probabilities predicted by both methods, revealed that the AI group achieved an area under the ROC curve (AUC) of 0.727, with a sensitivity of 90.27% and a specificity of 58.46%. In comparison, the physician-reading group had an AUC of 0.737, with a sensitivity of 83.19% and a specificity of 66.15%. Our findings demonstrate that the AI diagnostic system effectively calculates malignancy probabilities for lung nodules, highlighting its significant predictive potential. This system can serve as a valuable adjunct tool for clinicians and imaging physicians in the diagnostic process.

Clinical characteristics and risk factors associated with diverse manifestations of COVID-19 in patients aged 85 years and older.

The goal of this study is to assess the clinical attributes exhibited by patients aged 85 years and older who present different manifestations of COVID-19, and to examine the factors influencing the classification of the disease severity. This retrospective study was conducted at a single center, encompassing an analysis of clinical data obtained from patients with COVID-19 admitted to a general geriatric hospital in Hangzhou, Zhejiang, China, during the period from December 20, 2022, to February 1, 2023. The study focused on 91 eligible patients whose disease severity was compared based on the imaging findings. A total of 91 patients aged 85 years and older, with a median age of 92, including 46 males, 10 exhibiting mild symptoms, 48 moderate cases, and 33 severe cases met the inclusion criteria. Notably, disease severity displayed a significant correlation with age (p < 0.011). All patients presented with complicated chronic underlying conditions and a history of prolonged medication use. Rheumatic immune diseases (p = 0.040) and endocrine medications, primarily hypoglycemic agents (p = 0.034), exhibited statistical significance. Additionally, markers such as lactate dehydrogenase (LDH) (p = 0.030), interleukin 6 (IL-6) (p = 0.014), and D-dimer (p = 0.007) revealed significant associations with disease severity. Chest computed tomography scans predominantly revealed inflammatory features (n = 81, 89.0%). Notably, patients classified as having mild or moderate conditions exhibited eventual improvement, while 13 patients (39.4%) among the severe cases succumbed to the disease. The classification of disease among patients aged 85 years or older old is correlated with advanced age, concurrent rheumatic immune diseases, and prolonged administration of endocrine medications. Furthermore, patients with elevated levels of LDH, IL-6, and D-dimer demonstrated a higher propensity for developing severe diseases.

SARS-CoV2 pneumonia patients admitted to the ICU: Analysis according to clinical and biological parameters and the extent of lung parenchymal lesions on chest CT scan, a monocentric observational study

Background CT-scan and inflammatory and coagulation biomarkers could help in prognostication of COVID-19 in patients on ICU admission. Objective The objectives of this study were to measure the prognostic value of the extent of lung parenchymal lesions on computed tomography (CT) and of several coagulation and inflammatory biomarkers, and to explore the characteristics of the patients depending on the extent of lung parenchymal lesions. Design Retrospective monocentric observational study achieved on a dataset collected prospectively. Setting Medical ICU of the university hospital of Clermont-Ferrand, France. Patients All consecutive adult patients aged ≥18 years admitted between 20 March, 2020 and 31 August, 2021 for COVID-19 pneumonia. Interventions Characteristics at baseline and during ICU stay, and outcomes at day 60 were recorded. The extent of lung parenchyma lesions observed on the chest CT performed on admission was established by artificial intelligence software. Measurements Several clinical characteristics and laboratory features were collected on admission including plasma interleukin-6, HLA-DR monocytic–expression rate (mHLA-DR), and the extent of lung parenchymal lesions. Factors associated with day-60 mortality were investigated by uni- and multivariate survival analyses. Results 270 patients were included. Inflammation biomarkers including the levels of neutrophils, CRP, ferritin and Il10 were the indices the most associated with the severity of the extent of the lung lesions. Patients with more extensive lung parenchymal lesions (≥ 75%) on admission had higher CRP serum levels. The extent of lung parenchymal lesions was associated with a decrease in the PaO2/FiO2 ratio(p&lt;0.01), fewer ventilatory-free days (p = 0.03), and a higher death rate at day 60(p = 0.01). Extent of the lesion of more than 75% was independently associated with day-60 mortality (aHR = 1.72[1.06; 2.78], p = 0.03). The prediction of death at day 60 was improved when considering simultaneously biological and radiological markers obtained on ICU admission (AUC = 0.78). Conclusions The extent of lung parenchyma lesions on CT was associated with inflammation, and the combination of coagulation and inflammatory biomarkers and the extent of the lesions predicted the poorest outcomes.

Utilizing Un-enhanced Chest Computed Tomography Screening for Blunt Trauma Surgery Decisions.

Background Blunt chest trauma is a common and potentially life-threatening condition that requires prompt assessment for potential surgical intervention. Computed tomography (CT) of the chest has emerged as a valuable tool due to its heightened sensitivity and specificity in detecting thoracic injuries compared to conventional chest radiography. Objective This study aims to assess the impact of non-contrast CT chest findings on surgical decision-making and compare these findings with those from chest radiographs. Methods The study was conducted at the Accident and Emergency Department of Medical Teaching Institute, Lady Reading Hospital, Peshawar, Khyber Pakhtunkhwa, Pakistan. Patients of all ages and genders who presented with blunt chest trauma were included. Non-contrast CT chest scans were used as an initial screening tool and compared with traditional chest radiographs. Data collected included patient demographics, mechanism of injury, diagnostic findings, and treatment decisions. Imaging was performed using a GE Optima 16-slice scanner (Medsystems Sp. z o.o., Lublin, Poland). Results The study included 246 patients, of whom 210 (85.4%) were males. The most common age group was 50 years or older, comprising 71 (28.9%) of the sample. The predominant mechanism of trauma was road traffic accidents, reported by 188 (76.4%) patients. Hemopneumothorax was detected in 121 (49.2%) patients on CT scans compared to 34 (13.8%) patients on chest radiographs. On chest radiograph, the pneumothorax component was missed in 43 (17.5%) patients, and the hemothorax component was not detected in 21 (8.5%) patients. Patient management included conservative management in 30 (12.2%) cases and surgical intervention in the form of unilateral tube thoracostomy in 173 (70.3%) patients or bilateral tube thoracostomy in 43 (17.5%) patients. Conclusion Our study supports the use of non-contrast CT scans as a reliable diagnostic tool for blunt chest trauma, consistent with current literature. This approach facilitates prompt management decisions, particularly for initiating tube thoracostomy based on findings of pneumothorax and hemothorax. The rarity of mediastinal great vessel trauma further justifies minimizing routine contrast use, thereby enhancing the efficiency of trauma evaluations.

The pleural gradient does not reflect the superimposed pressure in patients with class III obesity

BackgroundThe superimposed pressure is the primary determinant of the pleural pressure gradient. Obesity is associated with elevated end-expiratory esophageal pressure, regardless of lung disease severity, and the superimposed pressure might not be the only determinant of the pleural pressure gradient. The study aims to measure partitioned respiratory mechanics and superimposed pressure in a cohort of patients admitted to the ICU with and without class III obesity (BMI ≥ 40 kg/m2), and to quantify the amount of thoracic adipose tissue and muscle through advanced imaging techniques.MethodsThis is a single-center observational study including ICU-admitted patients with acute respiratory failure who underwent a chest computed tomography scan within three days before/after esophageal manometry. The superimposed pressure was calculated from lung density and height of the largest axial lung slice. Automated deep-learning pipelines segmented lung parenchyma and quantified thoracic adipose tissue and skeletal muscle.ResultsN = 18 participants (50% female, age 60 [30–66] years), with 9 having BMI < 30 and 9 ≥ 40 kg/m2. Groups showed no significant differences in age, sex, clinical severity scores, or mortality. Patients with BMI ≥ 40 exhibited higher esophageal pressure (15.8 ± 2.6 vs. 8.3 ± 4.9 cmH2O, p = 0.001), higher pleural pressure gradient (11.1 ± 4.5 vs. 6.3 ± 4.9 cmH2O, p = 0.04), while superimposed pressure did not differ (6.8 ± 1.1 vs. 6.5 ± 1.5 cmH2O, p = 0.59). Subcutaneous and intrathoracic adipose tissue were significantly higher in subjects with BMI ≥ 40 and correlated positively with esophageal pressure and pleural pressure gradient (p < 0.05). Muscle areas did not differ between groups.ConclusionsIn patients with class III obesity, the superimposed pressure does not approximate the pleural pressure gradient, which is higher than in patients with lower BMI. The quantity and distribution of subcutaneous and intrathoracic adiposity also contribute to increased pleural pressure gradients in individuals with BMI ≥ 40. This study introduces a novel physiological concept that provides a solid rationale for tailoring mechanical ventilation in patients with high BMI, where specific guidelines recommendations are lacking.

Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV1 Decline in COPD.

Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD). We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88). Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.

Automatic Quantification of Abnormal Lung Parenchymal Attenuation on Chest Computed Tomography Images Using Densitometry and Texture-based Analysis.

To compare texture-based analysis using convolutional neural networks (CNNs) against lung densitometry in detecting chest computed tomography (CT) image abnormalities. A U-NET was used for lung segmentation, and an ensemble of 7 CNN architectures was trained for the classification of low-attenuation areas (LAAs; emphysema, cysts), normal-attenuation areas (NAAs; normal parenchyma), and high-attenuation areas (HAAs; ground-glass opacities, crazy paving/linear opacity, consolidation). Lung densitometry also computes (LAAs, ≤-950HU), NAAs (-949 to -700HU), and HAAs (-699 to -250HU). CNN-based and densitometry-based severity indices (CNN and Dens, respectively) were calculated as (LAA+HAA)/(LAA+NAA+HAA) in 812 CT scans from 176 normal subjects, 343 patients with emphysema, and 293 patients with interstitial lung disease (ILD). The correlation between CNN-derived and densitometry-derived indices was analyzed, alongside a comparison of severity indices among patient subgroups with emphysema and ILD, using the Spearman correlation and ANOVA with Bonferroni correction. CNN-derived and densitometry-derived severity indices (SIs) showed a strong correlation (ρ=0.90) and increased with disease severity. CNN-SIs differed from densitometry SIs, being lower for emphysema and higher for moderate to severe ILD cases. CNN estimations for normal attenuation areas were higher than those from densitometry across all groups, indicating a potential for more accurate characterization of lung abnormalities. CNN outputs align closely with densitometry in assessing lung abnormalities on CT scans, offering improved estimates of normal areas and better distinguishing similar abnormalities. However, this requires higher computing power.

Association Between Coronary Calcium Detection on Chest Computed Tomography and Ischemic Cardiovascular Events and Mortality in Asymptomatic Chronic Obstructive Pulmonary Disease Patients. Systematic Review of the Literature

IntroductionLiterature suggests that there is a close relationship between chronic obstructive pulmonary disease (COPD) and cardiovascular (CV) disease. The aim of this study was to assess whether the presence of coronary calcium (CC) on chest computed tomography (CT) in asymptomatic COPD patients is associated with an increased risk of CV events and mortality. Material and methodsA systematic review of the literature was performed following PRISMA recommendations. Studies published in the last 20 years in four databases (PubMed, Web of Science, Embase and MEDLINE) were included. ResultsThree hundred fifty articles were identified, eight of them met the selection criteria. The included studies, conducted between 2013 and 2024, were predominantly multicentre cohort studies. The meta-analysis showed that the presence of CC on chest CT of COPD patients is an independent predictor of CV events (hazard ratio 1.44, 95% CI 1.22–1.70) and associated with an increased mortality during the follow-up period (hazard ratio 1.57, 95% CI 1.35–1.83). ConclusionsOur analysis suggests that the identification of CC on chest CT scans of COPD patients may be useful in the early detection and treatment of CV disease in asymptomatic patients. Prospective, multicentre studies confirming our findings are needed to explore the potential impact of early detection and treatment of CV risk in COPD patients.

Wolf in sheep’s clothing: a case of primary lung adenosquamous carcinoma mimicking traumatic pulmonary pseudocyst

BackgroundTraumatic pulmonary pseudocyst is a rare “cystlike” lung lesion that typically develops following blunt chest trauma. It differs from lung cancer associated with cystic airspaces in terms of pathogenic mechanisms, clinical manifestations, and radiological features. Furthermore, there are few reports of the diagnostic bias between traumatic pulmonary pseudocyst and lung cancer associated with cystic airspaces. Here, we present a rare case of lung cancer associated with cystic airspaces that mimicks traumatic pulmonary pseudocyst.Case presentationA 61-year-old man with no chest medical or surgical history, no chest radiologic examination within the last five years, and no smoking history had an air-filled “cystlike” lesion surrounded by solid components and ground-glass opacities in the middle third of the right upper lobe of the lung during a computed tomography evaluation following blunt chest trauma. He was initially diagnosed with traumatic pulmonary pseudocyst and treated conservatively. On the third post-trauma day, he experienced hemoptysis, which was successfully treated with intravenous hemostatic medication. On the ninth post-trauma day, he exhibited a significant hemoptysis and a moderate dyspnea. A subsequent chest computed tomography scan demonstrated that the solid components had entered the lesion’s cavity and significantly expanded, and the surrounding ground-glass opacities had slightly enlarged. A contrast-enhanced chest computed tomography scan and a three-dimensional reconstruction computed tomography image confirmed that the solid components were a hematoma caused by damage to the right upper pulmonary vein. A right upper lobectomy was performed based on the concern about severe intrapulmonary bleeding. An intraoperative frozen section analysis showed significant bleeding in the lung parenchyma. Adenosquamous carcinoma was unexpectedly identified during the postoperative pathological examination of the resected specimen. A diagnosis of primary lung adenosquamous carcinoma was made. He was discharged on the seventh postoperative day and followed up for two years without any recurrence.ConclusionsThe potential of lung cancer associated with cystic airspaces should be considered for “cystlike” lung lesions discovered in elderly patients after blunt chest trauma. A comprehensive review of the medical history, meticulous analysis of the radiological findings, and close monitoring can help clinicians reduce the risk of diagnostic bias.

A Cross-Sectional Study Conducted in Tehran, Iran, Analyzed the Typical Patterns of Primary Lung CT Scans in Critically Ill COVID-19 Patients Using a Semi-Quantitative Scoring System

Background: Given the ongoing COVID-19 global pandemic and the potential for respiratory failure in several COVID-19 patients, early diagnosis and timely treatment are of paramount importance. Objectives: In this context, we aimed to investigate the role of initial chest computed tomography (CT) in predicting the severity of COVID-19. Methods: The study, conducted at Imam Hossein Hospital between March 6, 2020, and May 6, 2020, was cross-sectional in nature. All patients diagnosed with severe to critical COVID-19 underwent high-resolution chest CT (HRCT), and the findings of the chest CT were meticulously analyzed using a semi-quantitative scoring system. Results: Out of 47 patients with severe to critical COVID-19, 72.4% were male, with a mean age of 62 ± 14 years. The most common chest CT findings were ground-glass opacity (55%) and consolidation (30%). Bilateral involvement was observed in all patients, with the most frequently affected areas being the peribronchovascular distribution (76%) and peripheral distribution (74%). Most patients had pulmonary involvement across all five lobes, with scores ranging from a minimum of 3.25 to a maximum of 21.25 (CI: 12 - 15; mean: 13.25). Conclusions: This study suggests a relationship between the initial chest CT scan findings and the severity of respiratory disease in COVID-19 patients. However, further studies are needed to confirm these findings.

The Clinical Characteristics, Treatment and Prognosis of Tuberculosis-Associated Chronic Obstructive Pulmonary Disease: A Protocol for a Multicenter Prospective Cohort Study in China.

Tuberculosis and chronic obstructive pulmonary disease (COPD) are significant public health challenges, with pulmonary tuberculosis recognized as a pivotal risk factor for the development of COPD. Tuberculosis-associated COPD is increasingly recognized as a distinct phenotype of COPD that potentially exhibits unique clinical features. A thorough understanding of the precise definition, clinical manifestations, prognosis, and most effective pharmacological strategies for tuberculosis-associated COPD warrants further investigation. This prospective, observational cohort study aims to enroll over 135 patients with tuberculosis-associated COPD and 405 patients with non-tuberculosis-associated COPD, across seven tertiary hospitals in mainland China. The diagnosis of tuberculosis-associated COPD will be established based on the following criteria: (1) history of pulmonary tuberculosis with standard antituberculosis treatment; (2) suspected pulmonary tuberculosis with radiological evidence indicative of tuberculosis sequelae; (3) no definitive history of pulmonary tuberculosis but with positive interferon-gamma release assay results and radiological signs suggestive of tuberculosis. At baseline, demographic information, medical history, respiratory questionnaires, complete blood count, interferon-gamma release assays, medications, spirometry, and chest computed tomography (CT) scans will be recorded. Participants will be followed for one year, with evaluations at six-month intervals to track the longitudinal changes in symptoms, treatment, lung function, and frequencies of COPD exacerbations and hospitalizations. At the final outpatient visit, additional assessments will include chest CT scans and total medical costs incurred. The findings of this study are expected to delineate the specific characteristics of tuberculosis-associated COPD and may propose potential treatment options for this particular phenotype, potentially leading to improved clinical management and patient outcomes.

Antimoniosis: Radiological Insights into a Rare Pneumoconiosis in Miners.

Pneumoconiosis describes diseases caused by the accumulation of inorganic dust particles in the lungs, leading to tissue damage. The diagnosis relies on a history of exposure and compatible radiological findings. We aimed to investigate the radiological findings in individuals exposed to antimony-inert dust relative to their working periods. Fifty-six symptomatic male antimony miners were retrospectively evaluated for demographics and chest computed tomography (CT) scans. The demographic and radiological data of patients with a history of antimony mining, who presented at our pulmonary clinic between June 2017 and June 2023, were analyzed according to the duration of exposure. The study included 56 male patients with a mean age of 58.5±13.02 years and a mean exposure duration of 13.63 ± 6.82 years. CT scans showed that 73.2% (n=41) had upper and middle lung zone involvement, and 55.4% (n=31) had extensive involvement. Micronodules with centriacinar ground-glass opacities were the most common finding (n=37, 66.1%), followed by nodular opacities with irregular margins (n=22, 39.3%) and solid micronodules (n=20, 35.7%). Patients with over 20 years of exposure had significantly higher rates of respiratory and cardiovascular disease (p<0.05). Increased exposure time correlated with more extensive parenchymal involvement and higher rates of calcification in mediastinal lymph nodes, solid micronodules, nodular opacities with irregular margins, honeycombing, and conglomerate mass appearance. Radiological findings in pneumoconiosis generally worsen with longer exposure. Given the scarcity of up-to-date information on antimony pneumoconiosis, further studies focusing on radiological findings and chemical analyses of those exposed to antimony mine dust are essential to identify related pathologies.

Incidental pulmonary nodules: Natural language processing analysis of radiology reports

BackgroundPulmonary nodules are a common incidental finding on chest Computed Tomography scans (CT), most of the time outside of lung cancer screening (LCS). We aimed to evaluate the number of incidental pulmonary nodules (IPN) found in 1 year in our hospital, as well as the follow-up (FUP) rate and the clinical and radiological features associated with FUP. MethodsWe trained a Natural Language Processing (NLP) tool to identify the transcripts mentioning the presence of a pulmonary nodule, among a large population of patients from a French hospital. We extracted nodule characteristics using keyword analysis. NLP algorithm accuracy was determined through manual reading from a sample of our population. Electronic health database and medical record analysis by clinician allowed us to obtain information about FUP and cancer diagnoses. ResultsIn this retrospective observational study, we analyzed 101,703 transcripts corresponding to the entire CTs performed in 2020. We identified 1,991 (2 %) patients with an IPN. NLP accuracy for nodule detection in CT reports was 99 %. Only 41 % received a FUP between January 2020 and December 2021. Patient age, nodule size, and the mention of the nodule in the impression part were positively associated with FUP, while nodules diagnosed in the context of COVID-19 were less followed. 36 (2 %) lung cancers were subsequently diagnosed, with 16 (45 %) at a non-metastatic stage. ConclusionsWe identified a high prevalence of IPN with a low FUP rate, encouraging the implementation of IPN management program. We also highlighted the potential of NLP for database analysis in clinical research.

Early temporal changes on CT chest in patients with COVID-19 pneumonia

Introduction: Computed tomography (CT) scan of the chest helps in the evaluation, prognostication, and follow-up of patients with Corona virus disease-19 (COVID-19) pneumonia.Objectives: To study the early temporal evolution of findings on CT chest in patients with COVID-19 pneumonia at 6 weeks follow up.Material and methods: A retrospective analysis was performed on 40 patients with confirmed COVID-19 pneumonia who underwent baseline chest CT scans followed by a repeat scan at six weeks. Patients were quantitatively assessed using an objective CT severity score and qualitatively assessed using imaging parameters like consolidation, ground glass opacities (GGOs), parenchymal bands, septal thickening, nodules and crazy paving pattern.Results: Mean age of our study population was 58.5 ± 10.2 years with 80% being males. The baseline CT scan revealed a total median CT severity score interquartile range (IQR) of 16/25 (range: 14-20) while the follow-up scan after six weeks revealed a median score (IQR) of 14/25 (range: 8-19). This difference was statistically significant (p=0.04). The difference in CT severity scores of bilateral upper lobes was statistically significant (p=0.04 on right side and p=0.009 on left side); however, the difference in scores of bilateral lower lobes was not statistically significant (p&gt;0.05). The common imaging patterns on baseline imaging were GGO (97.5%), con-solidation (85%), septal thickening (85%), parenchymal bands (60%) and nodules (32.5%), which showed an interval resolution in the follow-up CT scan.Conclusion: Most of the patients with COVID-19 pneumonia showed interval resolution of lung findings seen on short-term follow-up CT scan.