Chest CT Scan Research Articles

Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of -950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (-950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete.

ASPERGILLOMA PADA TUBERKULOSIS PARU: LITERATURE REVIEW

Indonesia ranking among the top six countries with the highest TB burden. In the aftermath of pulmonary TB, fungal infections, particularly those caused by Aspergillus species, have emerged as a notable complication. The predominant species, Aspergillus fumigatus, is responsible for a spectrum of conditions ranging from invasive to noninvasive aspergillosis. This literature review aims to provide an exhaustive analysis of post-TB pulmonary aspergilloma, a clinical condition characterized by the growth of Aspergillus fungal balls within pre-existing lung cavities. Utilizing databases such as PubMed, Science Direct, EBSCO, Emerald, Scopus, and Google Scholar, and employing keywords such as "tuberculosis," "aspergillus," "aspergilloma," and "pulmonary," we meticulously applied the PICO framework and the CRAAP (Currency, Relevance, Authority, Accuracy, and Purpose) criteria to select 20 pertinent articles. The findings indicate that pulmonary aspergilloma manifests in approximately 11% to 17% of individuals post-TB treatment, presenting a clinical spectrum from asymptomatic to severe, life-threatening hemoptysis. Classification into simple and complex aspergilloma is based on specific clinical and radiological criteria. Diagnosis is contingent upon the identification of clinical symptoms, assessment of risk factors, and the corroboration of Aspergillus infection through radiological (chest X-ray and CT scan) and serological or microbiological evidence. Management of pulmonary aspergilloma entails non-operative and operative strategies, with surgical intervention being the definitive treatment, especially in cases of recurrent or massive hemoptysis. The review underscores the persistent challenge of TB in Indonesia, exacerbated by secondary fungal infections such as aspergillosis among pulmonary TB survivors. Aspergillus fumigatus is identified as the primary fungal pathogen, leading to pulmonary aspergilloma.

Abstract 887: Fast, interactive, AI-assisted 3D lung tumour segmentation

Abstract Segmentation of lung tumors is an important step in the planning of clinical treatment and operative procedures. Automatic segmentation of advanced lung tumors is challenging due to local invasion and heterogeneous metastasis. Fully automatic deep learning models often fail to accurately delineate advanced disease boundaries and do not allow the user to provide hints to improve segmentation performance. In contrast, interactive segmentation accepts user inputs and is an efficient way to segment lesions while reducing radiologist effort. This paper describes an interactive segmentation approach for lung lesion segmentation in CT scans. Our pipeline is built atop the SwinUNETR neural network architecture and can be expanded to support alternate models. The backbone model accepts radiologists' hints in the form of mouse clicks that indicate whether a location on the scan corresponds to a lesion. Our approach has the following novel contributions: (1) Context-aware encoding of guidance clicks by combining geodesic and Gaussian smoothing, resulting in improved segmentation of lesion boundaries. (2) An iterative prompting strategy to achieve higher accuracy with fewer clicks at inference time. (3) Guidance-aware Conditional Random Fields to refine the produced segmentation masks. (4) Volume cropping around guidance clicks at inference to improve segmentation precision and inference speed. (5) Linearly scaling our approach on multiple GPUs, to improve inference speed. On a public non-small cell lung cancer dataset predominantly containing advanced stage cancers (68\% stage III, and 27\% stage IV), our interactive approach delivers a Dice score of 0.74 with a single click, a 21\% improvement from the fully automatic approach (Dice Score = 0.61), and a Dice score of 0.81 with 5 clicks, which is a 33\% improvement. Inference speed is an important metric for an interactive pipeline. On a typical 350 $\times$ 350 $\times$ 150 voxel chest CT scan volume, our interactive approach infers 5 times faster than the original fully automated approach on a single GPU. After scaling to 4 GPUs, we infer in only 0.5 seconds; 20 times faster than the original approach. Qualitative evaluation from three independent radiologists indicates that our interactive pipeline significantly improved their standard clinical segmentation experience. Citation Format: Mayank Patwari, Yi Wei, Meng Xu, Zhenning Zhang, Konstantinos Sidiropoulos, Balaji Selvaraj, Georgia Hughes, Nadine Garibli, Kamsiriochukwu Ojiako, Mohan Lella, Leon Fedden, James Parkin, Michael Parker, Shaneil Patel, Qin Li, Kedar Patwardhan. Fast, interactive, AI-assisted 3D lung tumour segmentation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 887.

Acute Exacerbation of Idiopathic Pulmonary Fibrosis With Concurrent Cardiac Amyloidosis: A Technetium Pyrophosphate Study.

Amyloidosis presents a diagnostic challenge, particularly when concomitant with severe conditions like acute exacerbations of idiopathic pulmonary fibrosis (IPF). In this report, we detail the case of a 73-year-old patient with acute exacerbation of IPF and simultaneous emergence of cardiac amyloidosis. The patient's clinical journey began with persistent exertional dyspnea, progressing to hypoxemia on admission. Chest CT scans showed extensive ground-glass opacities, consolidations, and pre-existing honeycombing-like cysts and reticular shadows, accompanied by a right-sided pleural effusion. The therapeutic strategy for acute exacerbation of IPF encompassed methylprednisolone pulse therapy, tacrolimus, and nintedanib, augmented with intravenous immunoglobulin and recombinant thrombomodulin. Concurrently, heart failure with preserved ejection fraction was managed with a pharmacological trio: empagliflozin, diuretics, and eplerenone. A hypertrophied heart and low limb voltage prompted an investigation for cardiac amyloidosis, which 99mTechnetium pyrophosphate (99mTc-PYP) scintigraphy confirmed, yielding a probable diagnosis. Following steroid tapering, the patient was discharged home. This case prompted an investigation into the potential role of amyloidosis in pulmonary pathology. Our retrospective review of 10 patients, including four with cardiac amyloidosis, who underwent 99mTc-PYP scintigraphy, revealed a nonsignificant yet notable trend of increased pulmonary accumulation in cardiac amyloidosis cases (median (interquartile range): 5.4×104 (5.3-13.1×104) vs. 3.6×104 (2.4-5.1×104), p=0.0667). Notably, the pulmonary counts in this patient exceeded the negative cohort's mean values, hinting at a possible contribution of amyloid deposition to pulmonary pathology. This study, pioneering in evaluating lung field accumulation of 99mTc-PYP in cardiac amyloidosis, may provide novel insights into the influence of amyloidosis on pulmonary conditions.

Diagnostic Accuracy in Detecting Fungal Infection with Ultra-Low-Dose Computed Tomography (ULD-CT) Using Filtered Back Projection (FBP) Technique in Immunocompromised Patients.

Purpose: To compare the accuracy of ultra-low-dose (uLDCT) to standard-of-care low-dose chest CT (LDCT) in the detection of fungal infection in immunocompromised (IC) patients. Method and Materials: One hundred IC patients had paired chest CT scans performed with LDCT followed by uLDCT. The images were independently reviewed by three chest radiologists who assessed the image quality (IQ), diagnostic confidence, and detection of major (macro nodules, halo sign, cavitation, consolidation) and minor (4-10 mm nodules, ground-glass opacity) criteria for fungal disease using a five-point Likert score. Discrepant findings were adjudicated by a fourth chest radiologist. Box-whisker plots were used to analyze IQ and diagnostic confidence. Inter-rater reliability was assessed using interclass correlation coefficients (ICCs). The statistical difference between LDCT and uLDCT results was assessed using Wilcoxon paired test. Results: Lung reconstructions had IQ and diagnostic confidence scores (mean ± std) of 4.52 ± 0.47 and 4.63 ± 0.51 for LDCT and 3.85 ± 0.77 and 4.01 ± 0.88 for uLDCT. The images were clinically acceptable except for uLDCT in obese patients (BMI ≥ 30 kg/m2), which had an IQ ranking from poor to excellent (scores 1 to 5). The accuracy in detecting major and minor radiological findings with uLDCT was 96% and 84% for all the patients. The inter-rater agreements were either moderate, good, or excellent, with ICC values of 0.51-0.96. There was no significant statistical difference between the uLDCT and LDCT ICC values (p = 0.25). The effective dose for uLDCT was one quarter that of LDCT (CTDIvol = 0.9 mGy vs. 3.7 mGy). Conclusions: Thoracic uLDCT, at a 75% dose reduction, can replace LDCT for the detection of fungal disease in IC patients with BMI < 30.0 kg/m2.

MCFCN: Multi-scale capsule-weighted fusion classification network for lung disease classification based on chest CT scans

Aim and scopeThis paper aims to propose a Multi-scale Capsule-weighted Fusion Classification Network (MCFCN), a classification model for automatic diagnosis of lung lesions by CT scanning. BackgroundThe automatic diagnosis of lung lesions based on chest CT scans plays a crucial role in assisting doctors to identify suspicious cases quickly and accurately. However, existing methods struggle to differentiate lesions with similar morphologies, and current feature extraction techniques lack the ability to effectively highlight small-scale targets in a large-scale environment, leading to incomplete extraction of subtle features and ultimately compromising the classification performance. MethodThe MCFCN employs a dynamic routing clustering algorithm to emphasize small-scale features, preventing feature loss. Additionally, a scale difference fusion network is utilized to extract precise position scaling parameters by incorporating weighted fusion of information from different scales. ResultsMCFCN achieves an accuracy of 99.41% for COVID-19 classification, 93.33% for CAP classification, and 100% for Normal classification, with an overall accuracy of 98.36%. ConclusionExperimental results on the target dataset demonstrate that MCFCN outperforms state-of-the-art methods. In the future, this model can be further explored and optimized to enhance its application value in clinical practice

Evaluation of clinical characteristics and risk factors associated with Chlamydia psittaci infection based on metagenomic next-generation sequencing

IntroductionPsittacosis is a zoonosis caused by Chlamydia psittaci, the clinical manifestations of Psittacosis range from mild illness to fulminant severe pneumonia with multiple organ failure. This study aimed to evaluate the clinical characteristics of Chlamydia psittaci infection diagnosed based on metagenomic next-generation sequencing(mNGS), as well as the risk factors affecting the progress of Chlamydia psittaci infection, in order to improve the effect of therapeutics.MethodsWe retrospectively analyzed the clinical data of patients infected with chlamydia psittaci in the First Affiliated Hospital of Nanchang University from January 2021 to December 2021. The patient’s past medical history, clinical manifestations, laboratory examinations, chest CT results, treatment status, and prognosis data were collected. we also investigated both the pathogenic profile characteristics and the lower respiratory tract microbiota of patients with Chlamydia psittaci pneumonia using mNGS.ResultsAll cases of Chlamydia psittaci in our research have been confirmed by mNGS. Among 46 cases of Chlamydia psittaci pneumonia, Poultry exposure was reported in 35 cases. In severe cases of Chlamydia psittaci pneumonia, Neutrophils, Procalcitonin (PCT), Lactate Dehydrogenase (LDH), Hydroxybutyrate Dehydrogenase (HBDH), Creatine Kinase Isoenzymes-B (CK-MB) and D-Dimer levels were remarkably higher than that of non-severe cases, except for lymphocytes (all P < 0.05). Chest CT scans showed Bilateral (77.8%), multiple lobar lungs (85.2%), pleural effusions (44.4%) involvement in those suffering from severe Chlamydia psittaci pneumonia, whereas its incidence was 0%, 21.1% and 10.5% in non-severe patients, respectively (P < 0.05). Multivariate analysis revealed that higher lymphocyte concentrations (OR 0.836, 95% CI 0.714–0.962, P = 0.041) were the only protective factor for survival. mNGS results indicated that 41.3% of patients (19/46) had suspected coinfections with a coinfection rate of 84.2% (16/19) in the severe group, much higher than that in the non severe group (p < 0.05). No significantly different profiles of lower respiratory tract microbiota diversity were found between non severe group and severe group.ConclusionA history of poultry exposure in patients can serve as an important basis for diagnosing Chlamydia psittaci pneumonia, and patients with severe Chlamydia psittaci pneumonia are more likely to develop elevated inflammatory biomarkers as well as elevated cardiac markers. Higher lymphocyte concentrations are protective factors associated with severe C. psittaci pneumonia. The higher proportion of patients with coinfections in our study supports the use of mNGS for comprehensive early detection of respiratory infections in patients with C. psittaci pneumonia.

Accuracy of Chest Xray in Hospitalized COVID-19 Patients

Introduction: At the end of the year 2019 a novel virus named SARS-CoV-2 caused Coronavirus Disease 19 (COVID-19), manifesting as lung infection which can result in severe pneumonia. The gold standard for diagnosis of the virus is the detection of viral RNA through reverse transcriptase PCR (RT-PCR). Materials and Methods: This study is a retrospective analysis of diagnostic yield of chest imaging modalities in the diagnosis of COVID-19 in patients admitted to the Imam Khomeini hospital, Tehran, Iran during march 2020 until July 2020. Results: 204 hospitalized patients with a mean age of 58.5 years, diagnosed with COVID-19 were enrolled in this study, who had their disease confirmed by PCR. About 20% of patients had normal O2 Saturation (above 93%) and 80% had low O2 Saturation. Also 70% of patients were hospitalized to the ICU. Among investigated patients, 87.6% had abnormal findings in their CXR. Also, 97.1% of patients had abnormal CT-Scan. In this study, the sensitivity of the CXR in the diagnosis of COVID-19 was 87.5% (CI 95%, 83 to 91) and the sensitivity of the chest CT-Scan was 97.1% (CI 95%, 94.8 to 99). Discussion: Utilizing CXR as a first-line imaging modality is recommended in many countries and clinical settings and chest CT-Scan is mainly reserved for other additional roles. This study reveals a CXR sensitivity of 87.5% which is in accordance with the recent literature (69-90%).

Evaluation of the efficacy of pretreatment chest CT markers in predicting response to neoadjuvant chemoradiotherapy in locally advanced non-small cell lung cancer (NSCLC)

Aim: To investigate baseline enhanced chest CT findings that may predict progression or response to neoadjuvant chemoradiotherapy.&#x0D; Materials and methods: Multiple parameters to be obtained from baseline enhanced chest CT scans of 140 patients with NSCLC who had baseline enhanced chest CT scans before neoadjuvant chemoradiotherapy were noted. In addition to CT features of tumour tissues, age, gender, tumour cell types, lymph node TNM stages, distant metastases on baseline enhanced chest CT, bronchial and vascular invasion were also evaluated. Chest CT findings and changes in tumour tissue at 3 and 6 months during neoadjuvant treatment were noted. Patients were operated after the end of neoadjuvant treatment. It was investigated which parameters could predict response to neoadjuvant treatment and which findings could predict progression.&#x0D; Results: Progression and mortality rates were found to be low in patients with remission (p

Subcutaneous air entrapment after subcutaneous implantable cardioverter defibrillator implantation evaluated by computed tomography.

Inappropriate shock (IAS) caused by subcutaneous air entrapment (AE) in an early period after subcutaneous implantable cardioverter defibrillator (S-ICD) implantation has been reported, however, no detailed data on air volume are available. We evaluated the subcutaneous air volume after implantation and its absorption rate one week after implantation. Patients who underwent S-ICD implantation in our hospital received chest CT scans immediately after implantation and followed up 1 week later. The total subcutaneous air volume, air around the generator, the distal electrode, and the proximal electrode within 3cm were calculated using a three-dimensional workstation. Fat areas at the level of the lower edge of the generator were also analyzed. Fifteen patients received CT immediately after implantation. The mean age was 45.6±17.9 (66.7% of men), and the mean body mass index was 24.3±3.3. The three-incision technique was applied in seven patients and two-incision technique was in the latter eight patients. The mean total subcutaneous air volume was 18.54±7.50mL. Air volume around the generator, the distal electrode, and the proximal electrode were 11.05±5.12, 0.72±0.72, and 0.88±0.87mL, respectively. Twelve patients received a follow-up CT 1 week later. The mean total subcutaneous air was 0.25±0.45mL, showing a 98.7% absorption rate. Although subcutaneous air was observed in all patients after S-ICD implantation, most of the air was absorbed within 1 week, suggesting a low occurrence of AE-related IAS after a week postoperation.

Optimized Deep Learning Algorithm for Predicting Pulmonary Nodules in CT Images

Lung cancer remains a significant global health challenge, demanding early detection for improved patient outcomes. In recent years, deep learning, notably Convolutional Neural Networks (CNNs), has emerged as a potent tool for lung cancer detection and diagnosis from medical imaging data. This research offers an extensive review of CNN-based approaches for lung cancer detection, highlighting their strengths, limitations, and potential clinical impact. The study discusses the methodology, covering data collection, preprocessing, model architecture selection, training, evaluation, and validation, alongside future directions and clinical implications. CNNs offer researchers and healthcare professionals avenues to augment early detection, personalized treatment planning, and ultimately, enhance patient care in lung cancer management. Through rigorous development and evaluation, CNN models trained on diverse datasets of chest X-rays or CT scans have demonstrated remarkable accuracy in identifying suspicious lung lesions indicative of cancer, often outperforming conventional methods. The proposed study utilizes the GoogleNet (Inception v1) CNN model to detect lung cancer. The performance of GoogleNet improved the accuracy of detection by approximately 4.29% compared to existing methods.

Baseline chest computed tomography for diagnosis of invasive aspergillosis in patients with acute myeloid leukaemia treated with intensive chemotherapy: A retrospective single-centre cohort study.

Invasive pulmonary aspergillosis (IPA) is a relatively common infection in patients with acute myeloid leukaemia (AML), and is associated with high mortality rates. Optimising early detection is key to reduce the burden of IPA in this population. In this retrospective cohort study, we evaluated the added value of baseline chest CT before start of classical induction chemotherapy. Adult patients receiving first-line intensive chemotherapy for AML were included if a baseline chest CT scan was available (±7 days). Data were collected from the electronic health record. IPA was classified using the EORTC/MSGERC 2020 consensus definitions. Between 2015 and 2019, 99 patients were included. During first-line treatment, 29/99 (30%) patients developed a probable IPA. Baseline chest CT was abnormal in 61/99 (62%) and 14/61 (23%) patients had typical radiological signs for IPA. An abnormal scan showed a trend towards higher risk for IPA (hazard ratio (HR): 2.12; 95% CI 0.95-4.84). Ground glass opacities were a strong predictor for developing IPA (HR 3.35: 95% CI 1.61-7.00). No probable/proven IPA was diagnosed at baseline; however, a bronchoalveolar lavage (BAL) at baseline was only performed in seven patients. Twelve-week mortality was higher in patients with IPA (7/26, 27% vs. 5/59, 8%; p = .024). Baseline chest CT scan could be an asset in the early diagnosis of IPA and contribute to risk estimation for IPA. In patients with an abnormal baseline CT, performing a BAL should be considered more frequently, and not only in patients with radiological findings typical for IPA.

Management of Pediatric Patients With Spontaneous Pneumomediastinum: A Retrospective Chart Review

PurposeGiven the paucity of data on pediatric spontaneous pneumomediastinum (SPM), management is largely informed by extrapolation from adult studies or personal clinical experience, resulting in significant heterogeneity. The purpose of this study was to describe how pediatric patients with SPM are clinically managed at our institution and propose a treatment algorithm. MethodsRetrospective chart review of pediatric patients with SPM from April 2002 to December 2021 at a single Canadian tertiary care center. Data on medical history, presentation characteristics, clinical management, and complications were gathered. Descriptive and inferential statistics were used to analyze data. ResultsWe identified 63 patients for inclusion, median age was 15 years. Twenty-nine patients were transferred from another facility. Most common presenting symptoms were chest pain (72.3%), shortness of breath (44.6%), and subcutaneous emphysema (21.5%). Initial workup included chest x-ray (93.6%), CT scan (20.6%), and upper GI series (7.9%). There was no difference in the number of initial tests between admitted and discharged patients (p = 0.10). Of admitted patients (n = 35), 31.4% had primary SPM (no underlying comorbidity/inciting event) and 68.8% were secondary SPM (underlying comorbidity/inciting event). No patients with primary SPM developed complications or recurrences. In contrast, 16.7% of those with secondary SPM developed complications and 54.2% had at least one additional intervention after admission. DiscussionThere is significant variability in diagnostic investigation and treatment of pediatric SPM at our center. Amongst primary SPM, additional tests did not change complication rate or recurrence, including those transferred from another facility. An expeditious treatment algorithm is warranted. Type of StudyRetrospective Cohort Study. Level of EvidenceIII.

Enhancing medical image segmentation with a multi-transformer U-Net.

Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach that combines the Swin Transformer and Deformable Transformer to enhance overall model performance. We leverage the Swin Transformer's window attention mechanism to capture local feature information and employ the Deformable Transformer to adjust sampling positions dynamically, accelerating model convergence and aligning it more closely with object shapes and sizes. By amalgamating both Transformer modules and incorporating additional skip connections to minimize information loss, our proposed model excels at rapidly and accurately segmenting CT or X-ray lung images. Experimental results demonstrate the remarkable, showcasing the significant prowess of our model. It surpasses the performance of the standalone Swin Transformer's Swin Unet and converges more rapidly under identical conditions, yielding accuracy improvements of 0.7% (resulting in 88.18%) and 2.7% (resulting in 98.01%) on the COVID-19 CT scan lesion segmentation dataset and Chest X-ray Masks and Labels dataset, respectively. This advancement has the potential to aid medical practitioners in early diagnosis and treatment decision-making.

Presentation, diagnosis, management and outcome of complicated hydatid cyst of the lung

Introduction: Introduction: The complicated hydatid cyst of the lung is defined as any cyst ruptured in the bronchi tree or in the pleural cavity, infected, and rarely in the pericardial cavity. The aim of this study was to evaluate the problems of complicated pulmonary hydatid cyst, including presentation, diagnosis choice of surgical methods. and complication of delay in surgical treatment of pulmonary hydatid cysts. Methods: Between 2016 and 2021, 192 operations operated for pulmonary hydatid cysts, 42 of these patients were complicated hydatid cyst. Presentations were cough, chest pain and dyspnea.Diagnosis tools were CXR and CT-scan of chest. surgical approach was a posterolateral thoracotomy and anterolateral thoracotomyin all patients; a phrenotomy in eight patients and one-stage bilateral thoracotomy in six patients. Results: In 18 patients (67.5%), there were single hydatid cysts; whereas 4 patients (32.5%) had multiple cysts.12 patients had bilateral cyst and 8 had both lung and liver and others organs, 6 patients had preoperative hydatid cyst history. Iatrogenic rupture of an intact hydatid cyst occurred in 4 patients. Extrathoracic involvement was apparent in 6 patients (27%). Intrathoracic but extrapulmonary involvement was apparent in 4 patients (16%). Conclusion: Complicated hydatid cyst may present with different clinical and radiologically manifestations as a primary lung tumor , pleural effusion ,empyema hydropneumothorax. In patients with suspicious lung masses in endemic area or history of a hydatid cyst in patients a complicated pulmonary hydatid cyst may be in differential diagnosis.

Association between body fat decrease during the first year after diagnosis and the prognosis of idiopathic pulmonary fibrosis: CT-based body composition analysis

BackgroundThe prognostic role of changes in body fat in patients with idiopathic pulmonary fibrosis (IPF) remains underexplored. We investigated the association between changes in body fat during the first year post-diagnosis and outcomes in patients with IPF.MethodsThis single-center, retrospective study included IPF patients with chest CT scan and pulmonary function test (PFT) at diagnosis and a one-year follow-up between January 2010 and December 2020. The fat area (cm2, sum of subcutaneous and visceral fat) and muscle area (cm2) at the T12-L1 level were obtained from chest CT images using a fully automatic deep learning-based software. Changes in the body composition were dichotomized using thresholds dividing the lowest quartile and others, respectively (fat area: -52.3 cm2, muscle area: -7.4 cm2). Multivariable Cox regression analyses adjusted for PFT result and IPF extent on CT images and the log-rank test were performed to assess the association between the fat area change during the first year post-diagnosis and the composite outcome of death or lung transplantation.ResultsIn total, 307 IPF patients (69.3 ± 8.1 years; 238 men) were included. During the first year post-diagnosis, fat area, muscle area, and body mass index (BMI) changed by -15.4 cm2, -1 cm2, and − 0.4 kg/m2, respectively. During a median follow-up of 47 months, 146 patients had the composite outcome (47.6%). In Cox regression analyses, a change in the fat area < -52.3 cm2 was associated with composite outcome incidence in models adjusted with baseline clinical variables (hazard ratio [HR], 1.566, P = .022; HR, 1.503, P = .036 in a model including gender, age, and physiology [GAP] index). This prognostic value was consistent when adjusted with one-year changes in clinical variables (HR, 1.495; P = .030). However, the change in BMI during the first year was not a significant prognostic factor (P = .941). Patients with a change in fat area exceeding this threshold experienced the composite outcome more frequently than their counterparts (58.4% vs. 43.9%; P = .007).ConclusionA ≥ 52.3 cm2 decrease in fat area, automatically measured using deep learning technique, at T12-L1 in one year post-diagnosis was an independent poor prognostic factor in IPF patients.

Pulmonary Involvement during Varicella in a Patient with Sickle Cell Disease: Diagnosis and Treatment

Introduction: Pulmonary involvement during chickenpox is one of the most serious complications. However, it can be difficult to distinguish between varicella pneumonia, bacterial superinfection and an acute chest syndrome. We report a case of varicella complicated by pneumonia in a young adult with sickle cell disease.&#x0D; Observation: It was a 24-year-old woman with sickle cell disease. Examination on admission revealed generalized skin lesions of varying ages, associated with scratching lesions. On day 3 of hospitalization, she presented an acute respiratory distress associated with fever (40°C). Thoracic CT angiography revealed diffuse pulmonary nodules and micronodules in both lung fields. Blood culture isolated Staphylococcus spp. The patient was put on oxygen therapy combined with antiviral treatment and antibiotic therapy. The course was marked by symptoms regression, with disappearance of respiratory distress on day 2 and apyrexia on day 3 of treatment. The follow-up chest CT scan carried out at month 4, came back normal.&#x0D; Conclusion: Although pneumonia is one of the most common complications of varicella, it can be difficult to diagnose in patients with sickle cell disease, considering the frequency of pulmonary involvement of various etiologies

Impact of vaccination status on clinical outcomes of hospitalized COVID-19 patients

IntroductionIt is important to identify the relationship between the COVID-19 vaccination status and the prognosis of this disease in hospitalized patients to gain a more accurate picture of their status and the effect of vaccination, as well as take necessary measures to improve their medical care. Thus, the present study was conducted to investigate the relationship between the vaccination status of hospitalized COVID-19 patients and the disease severity index in terms of clinical, imaging, and laboratory criteria.MethodsThis research is a descriptive-analytical cross-sectional study. the study population consisted of patients with a positive RT-PCR test for coronavirus, admitted to COVID-19 departments of teaching hospitals in Yazd, Iran, during two months in the sixth peak of COVID-19. The patients’ data comprised demographic information (age, sex, and underlying disease), clinical information (length of hospital stay, length of ICU stay, and vaccination status), disease outcome (mortality and intubation), laboratory information (ESR, CRP, and NLR), and imaging information (lung involvement percentage), and finally, the relationship between patients’ vaccination status and disease severity indices were analyzed with the chi-square test, independent t-test, and logistic regression analysis at a 95% confidence interval (CI).FindingsAccording to research findings, the duration of hospitalization was 5.25 ± 2.34 and 6.11 ± 3.88 days in groups of patients with complete and incomplete vaccination, respectively (P = 0.003). The lengths of ICU stay were 6 ± 4.63 and 5.23 ± 3.73 days in both groups of patients admitted to the ICU (P = 0.395). Furthermore, there were significant relationships between the ICU admission rates, endotracheal intubation, mortality rate, the lung involvement score in the chest CT scan, and the NLR with the vaccination status.Multivariate regression analysis indicated that DM, IHD, NLR, CT scan score and vaccination status were related to patients’ in-hospital mortality.ConclusionComplete vaccination of COVID-19 led to a milder disease in terms of clinical, imaging, and laboratory criteria of patients and decreased the possibility of hospitalization in ICUs, intubation, and mortality in patients.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Prediction of mortality in adult COVID-19 patients using chest CT severity scoring systems: A comparative analysis of different scores

Purpose: To compare the accuracy of mortality prediction of four CT severity scoring systems for COVID-19: CT severity score three levels, CT severity score, Total severity score, and Chest CT score. Methods: This was a retrospective study of 278 patients hospitalized with COVID-19 confirmed by a positive polymerase chain reaction (PCR) and in whom a CT scan was performed to assess the severity of lung involvement. This assessment was performed using four different scoring systems, including the CT severity score three levels, the CT severity score, the Total severity score, and the Chest CT score. Results: A total of 278 COVID-19 patients had chest CT scans, of whom 59 (21.22%) died and 219 (78.78%) survived. The ROC curves showed outstanding performance for the four chest CT severity scoring systems: 0.9580 for the CT severity score; 0.9532 for the CT severity score three levels; 0.9474 for the Total severity score; and 0.9327 for the Chest CT score. The comparison of these four ROC curves revealed no statistically significant difference between the four scoring systems (X2 = 3.89; p = 0.2740). Conclusion: The four chest CT severity scoring systems used predicted mortality in COVID-19 patients with excellent agreement and outstanding performance.