Chest Computed Tomography Scan Research Articles

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.

Familial Hypokalemic Periodic Paralysis Attack Following SARS‑Cov‑2 Infection: A Case Report

Familial hypokalemic periodic paralysis is a rare disorder that manifests manifests with the sudden onset of flaccid paralysis that is triggered by low levels of blood potassium, which can be caused by various factors such as, rest after intense exercise, or high-carbohydrate foods. This report presents cases of hypokalemic periodic paralysis attack triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. A 29-year-old male patient was admitted with quadriparesis, fever, shortness of breath, and sever hypokalemia. The patient had a history of three episodes of Familial hypokalemic periodic paralysis (HPP). Diagnostic tests, such as chest computed tomography scan and polymerase chain reaction test, confirmed SARS-CoV-2 infection. The patient was treated with potassium chloride infusion, spironolactone, and remdesivir, and was eventually discharged from the hospital.in conclusion, SARS-CoV-2 infection can potentially exacerbate HPP and should be considered a risk factor for its occurrence.

Weakly-supervised learning-based pathology detection and localization in 3D chest CT scans.

Recent advancements in anomaly detection have paved the way for novel radiological reading assistance tools that support the identification of findings, aimed at saving time. The clinical adoption of such applications requires a low rate of false positives while maintaining high sensitivity. In light of recent interest and development in multi pathology identification, we present a novel method, based on a recent contrastive self-supervised approach, for multiple chest-related abnormality identification including low lung density area ("LLDA"), consolidation ("CONS"), nodules ("NOD") and interstitial pattern ("IP"). Our approach alerts radiologists about abnormal regions within a computed tomography (CT) scan by providing 3Dlocalization. We introduce a new method for the classification and localization of multiple chest pathologies in 3D Chest CT scans. Our goal is to distinguish four common chest-related abnormalities: "LLDA", "CONS", "NOD", "IP" and "NORMAL". This method is based on a 3D patch-based classifier with a Resnet backbone encoder pretrained leveraging recent contrastive self supervised approach and a fine-tuned classification head. We leverage the SimCLR contrastive framework for pretraining on an unannotated dataset of randomly selected patches and we then fine-tune it on a labeled dataset. During inference, this classifier generates probability maps for each abnormality across the CT volume, which are aggregated to produce a multi-label patient-level prediction. We compare different training strategies, including random initialization, ImageNet weight initialization, frozen SimCLR pretrained weights and fine-tuned SimCLR pretrained weights. Each training strategy is evaluated on a validation set for hyperparameter selection and tested on a test set. Additionally, we explore the fine-tuned SimCLR pretrained classifier for 3D pathology localization and conduct qualitativeevaluation. Validated on 111 chest scans for hyperparameter selection and subsequently tested on 251 chest scans with multi-abnormalities, our method achieves an AUROC of 0.931 (95% confidence interval [CI]: [0.9034, 0.9557], -value < 0.001) and 0.963 (95% CI: [0.952, 0.976], -value < 0.001) in the multi-label and binary (i.e., normal versus abnormal) settings, respectively. Notably, our method surpasses the area under the receiver operating characteristic (AUROC) threshold of 0.9 for two abnormalities: IP (0.974) and LLDA (0.952), while achieving values of 0.853 and 0.791 for NOD and CONS, respectively. Furthermore, our results highlight the superiority of incorporating contrastive pretraining within the patch classifier, outperforming Imagenet pretraining weights and non-pretrained counterparts with uninitialized weights (F1 score = 0.943, 0.792, and 0.677 respectively). Qualitatively, the method achieved a satisfactory 88.8% completeness rate in localization and maintained an 88.3% accuracy rate against falsepositives. The proposed method integrates self-supervised learning algorithms for pretraining, utilizes a patch-based approach for 3D pathology localization and develops an aggregation method for multi-label prediction at patient-level. It shows promise in efficiently detecting and localizing multiple anomalies within a singlescan.

Increased Scan Speed and Pitch on Ultra-Low-Dose Chest CT: Effect on Nodule Volumetry and Image Quality.

Background and Objectives: This study's objective was to investigate the influence of increased scan speed and pitch on image quality and nodule volumetry in patients who underwent ultra-low-dose chest computed tomography (CT). Material and Methods: One hundred and two patients who had lung nodules were included in this study. Standard-speed, standard-pitch (SSSP) ultra-low-dose CT and high-speed, high-pitch (HSHP) ultra-low-dose CT were obtained for all patients. Image noise was measured as the standard deviation of attenuation. One hundred and sixty-three nodules were identified and classified according to location, volume, and nodule type. Volume measurement of detected pulmonary nodules was compared according to nodule location, volume, and nodule type. Motion artifacts at the right middle lobe, the lingular segment, and both lower lobes near the lung bases were evaluated. Subjective image quality analysis was also performed. Results: The HSHP CT scan demonstrated decreased motion artifacts at the left upper lobe lingular segment and left lower lobe compared to the SSSP CT scan (p < 0.001). The image noise was higher and the radiation dose was lower in the HSHP scan (p < 0.001). According to the nodule type, the absolute relative volume difference was significantly higher in ground glass opacity nodules compared with those of part-solid and solid nodules (p < 0.001). Conclusion: Our study results suggest that HSHP ultra-low-dose chest CT scans provide decreased motion artifacts and lower radiation doses compared to SSSP ultra-low-dose chest CT. However, lung nodule volumetry should be performed with caution for ground glass opacity nodules.

The value of computed tomography-based radiomics for predicting malignant pleural effusions.

Malignant pleural effusion (MPE) is a common clinical problem that requires cytological and/or histological confirmation obtained by invasive examination to establish a definitive diagnosis. Radiomics is rapidly evolving and can provide a non-invasive tool to identify MPE. We aimed to develop a model based on radiomic features extracted from unenhanced chest computed tomography (CT) images and investigate its value in predicting MPE. This retrospective study included patients with pleural effusions between January 2016 and June 2020. All patients underwent a chest CT scanning and medical thoracoscopy after artificial pneumothorax. Cases were divided into a training cohort and a test cohort for modelling and verifying respectively. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) were applied to determine the optimal features. We built a radiomics model based on support vector machines (SVM) and evaluated its performance using ROC and calibration curve analysis. Twenty-nine patients with MPE and fifty-two patients with non-MPE were enrolled. A total of 944 radiomic features were quantitatively extracted from each sample and reduced to 14 features for modeling after selection. The AUC of the radiomics model was 0.96 (95% CI: 0.912-0.999) and 0.86 (95% CI: 0.657~1.000) in the training and test cohorts, respectively. The calibration curves for model were in good agreement between predicted and actual data. The radiomics model based on unenhanced chest CT has good performance for predicting MPE and may provide a powerful tool for doctors in clinical decision-making.

Exploring bone density analysis on routine CT scans as a tool for opportunistic osteoporosis screening

The primary aim of this study was to evaluate computed tomography (CT)-based bone density analysis at the level of thoracic vertebra 12 (Th12) as a screening method for decreased bone density in patients admitted to the intensive care unit (ICU). Interobserver variability was analyzed. Secondary aims were to assess the prevalence of CT-based low bone density upon ICU admission in a cohort of COVID-19 patients and to assess the potential effect of long-term ICU stay on bone density in these patients. Retrospective single-center cohort study. ICU of the Leiden University Medical Center (LUMC), the Netherlands. Patients admitted to the ICU of the LUMC between March 1st, 2020 and February 1st, 2022 with a diagnosis of COVID-19, and a length of ICU stay of ≥ 21 days. In the included patients both baseline chest CT scans (obtained upon ICU admission) and follow-up chest CT scans (obtained ≥ 21 days after ICU admission) were available for analysis. A total of 118 CT scans in 38 patients were analyzed. There was a good interobserver variability, with an overall mean absolute difference (between measurements of three observers) of 9.7 Hounsfield Units (HU) and an intraclass correlation coefficient (ICC) of 0.93 (95% CI 0.88–0.96). The effect of intravenous contrast administration on bone density measurements was small (+ 7.5 HU (95% CI 3.4–11.5 HU)) higher in contrast enhanced CT images compared to non contrast enhanced CT images). Thirty-seven percent of patients had a bone density < 140 HU, suggestive of osteoporosis. No significant difference was found between bone density upon ICU admission and bone density at follow-up (≥ 21 days after ICU admission). Vertebral CT-based bone density analysis using routine CT scans is an easily applicable method to identify ICU patients with decreased bone density, which could enable enrollment in osteoporosis prevention programs. A high prevalence of low bone density was found in our cohort of ICU patients. There were no changes observed in bone density between baseline and follow-up measurements.

Cardiopulmonary crisis caused by bronchial blocker malposition in a patient with aberrant tracheal anatomy: a case report

BackgroundDouble-lumen tubes (DLTs) and bronchial blockers (BBs) can be used to establish one-lung ventilation (OLV) for thoracic surgery. BBs are a good alternative when DLTs are not suitable or patients have difficult airways. However, BBs are more prone to malposition, leading to adverse events.Case presentationWe present a 68-year-old male patient who was scheduled for thoracoscopic left lower lobectomy. The patient was not expected to have airway malformation preoperatively. When the DLT could not be inserted into the bronchus after general anesthesia induction, we used a BB to perform OLV. During surgery, malposition of the BB resulted in the development of an “incomplete balloon valve”, leading to a cardiopulmonary crisis.ConclusionsPreviewing chest computed tomography scans to assess the airway anatomy before thoracic surgery is essential. Three-dimensional reconstruction of the airway can provide a more intuitive assessment of airway anatomy. During OLV with BBs, we should pay attention to balloon malposition to prevent cardiopulmonary crises.