Chest CT Scan Research Articles

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.

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM)

PurposeFrequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM).Material and methodsIn a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired t-test, and Bland–Altman plots.ResultsPatients averaged 49.4 years and 100% were female with mean BMI 26.6 ± 6.1 kg/m2. Cyst score measured by AiCE reconstruction with SilverBeam filter correlated well with that of AIDR3D reconstruction with standard filter, with a 1.5% difference, and allowed for an 85.5% median radiation dosage reduction (0.33 mSv vs. 2.27 mSv, respectively, p < 0.001). Compared to standard filter with AIDR3D, SNR for SilverBeam AiCE images was slightly lower (3.2 vs. 3.1, respectively, p = 0.005).ConclusionSilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease.Clinical relevance statementRadiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm.Key Points• Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction.• SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT.• SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.Graphical abstract

Successful Surgical Management of Giant Mediastinal Teratoma – A Case Report of a Difficult Case without Any Post-Operative Complication

Introduction. Mediastinal teratoma is a rare germ cell tumor, located in anterior mediastinum and is lack of population-based study. It usually occurs in the age of 20-40 years, accounting for 15% of anterior mediastinal masses in adults and 25% of anterior mediastinal masses in children. It is frequently diagnosed accidentally on chest imaging due to its asymptomatic nature. Hereby, we presented a case of giant anterior mediastinum teratoma with complete surgical excision without any post-operative complication. This case is unique due to its uneventful result after excision. From our literature research, we successfully avoid the potential post operative complications of resecting this tumor such as bleeding, wound dehiscence and even death. It is due to early diagnosis, treatment and good intraoperative management of lesion &amp; hemodynamics. Case Report. A 21-year old male was referred from pulmonologist oncologist to our Thoracic Surgical Unit with complains of dyspnea and chest pain. Physical examination showed no lung sounds in (R) hemithorax and was dull to percussion, ictus cordis shifted 2 fingerbreadths towards left-side. Chest X-ray showed a large well-defined lesion in (R) hemithorax and chest CT-scan with contrast showed hypodense mass on (R) hemithorax adhering &amp; compressing the lung, diaphragm, pericardium &amp; chest wall, with estimation size of 60 x 20 x 12 cm, lesion displaced the heart. Patient was then treated with complete surgical excision and found a huge cystic tumor with a size of 60 x 20 x 12 cm adhering to all parts of (R) lung, diaphragm and pericardium. He underwent (R) pneumonectomy due to adhesion. Histologic examination was consistent with mature teratoma. Patient showed no complication after surgery and was discharged uneventfully from our hospital after 5 days. Discussion. Approximately 95% of benign teratomas arise in the anterior mediastinum. The tumor grows progressively, mostly asymptomatic, dyspnea and substernal chest pain are the most common symptoms if present. Productive Cough of hair or sebum is a pathognomonic sign. Chest radiograph typically reveals a well-circumscribed anterior mediastinal mass that often protrudes into one of the lung, they are usually large at the time of diagnosis. Complete surgical excision is a treatment of choice of mediastinal teratoma, which can be performed through median sternotomy or thoracotomy or thoracoscopy. Some patients require additional procedures (eg, lobectomy, pericardiectomy) for complete tumor resection. Conclusion. There is still limited data on mediastinal teratoma. Early referral &amp; treatment show good prognosis. Benign Mediastinal Teratomas was tricky to be diagnosed, when it is diagnosed, it is usually very large in size. Complete Surgical Excision can be performed safely without any complications.

A Comprehensive Exploration of Artificial Intelligence Methods for COVID-19 Diagnosis

INTRODUCTION: The 2019 COVID-19 pandemic outbreak triggered a previously unseen global health crisis demanding accurate diagnostic solutions. Artificial Intelligence has emerged as a promising technology for COVID-19 diagnosis, offering rapid and reliable analysis of medical data. OBJECTIVES: This research paper presents a comprehensive review of various artificial intelligence methods applied for the diagnosis, aiming to assess their effectiveness in identifying cases, predicting disease progression and differentiating from other respiratory diseases. METHODS: The study covers a wide range of artificial intelligence methods and with application in analysing diverse data sources like chest x-rays, CT scans, clinical records and genomic sequences. The paper also explores the challenges and limitations in implementing AI -based diagnostic tools, including data availability and ethical considerations. CONCLUSION: Leveraging AI’s potential in healthcare can significantly enhance diagnostic efficiency crisis management as the pandemic evolves.

Relationships between Size-specific Dose Estimate and Signal to Noise Ratio under Chest CT Examinations with Tube Current Modulation.

Exploring the relationship between the signal-to-noise ratio (SNR) of organs and size-specific dose estimate (SSDE) in tube current modulation (TCM) chest CT examination. Forty patients who received TCM chest CT scanning were retrospectively collected and divided into four groups according to the tube voltage and sexes. We chose to set up the region of interest (ROI) at the tracheal bifurcation and its upper and lower parts in slice images of the heart, aorta, lungs, paracranial muscles, and female breast, and the SNR of each organ was calculated. We also calculated the corresponding axial volume CT dose index (CTDIvolz) and axial size-specific dose estimate (SSDEz). The correlation analysis showed that the correlation between the SNR of the slice images of most organs and SSDEz was more significant than 0.8, and that between the SNR and CTDIvol was more significant than 0.7. The simple linear regression analysis results showed that when the sex is the same, the SNR of the same organ at 100kVp was higher than 120kVp, except for the lung. In multiple regression analysis, the result indicated that the determination coefficients of the SNR and SSDEz of the four groups were 0.934, 0.971, 0.905, and 0.709, respectively. In chest CT examinations with TCM, the correlation between the SNR of each organ in slice images and SSDEz was better than that of CTDIvolz. And when the SSDEz was the same, the SNR at 100 kVp was better than that at 120 kVp.

Pilot intervention to increase uptake of lung cancer screening through the emergency department

IntroductionThe goal of this study is to demonstrate the feasibility of referring patients for lung cancer screening (LCS) from the emergency department (ED) as a method to increase the uptake of LCS. MethodsThis is a single-arm pilot study at a large safety-net ED. ED LCS-eligible patients were offered a referral to our LCS clinic upon ED discharge. The primary outcome was the frequency at which patients connected with the LCS clinic. ResultsDuring the study period, 105 patients were approached; 26 (24.8%) participated. Reasons for non-enrollment include 29 (27.6%) who were not interested in research, 10 (9.5%) who did not speak English, and 40 (38.1%) who did not meet the pack-years criteria. Seventeen patients (65.4%, 17/26) connected with the LCS clinic, with 10 (38.5%) having been seen in the clinic and received a low dose computed tomography (LDCT) scan. Of the 17 that were connected with the clinic, 7 (26.9%) had a non-LDCT chest CT scan in the past year. Of those that were not seen in the clinic (n = 9), 4 (44.4%) were unreachable via 3 phone calls and a post-marked letter, and 3 (33.3%) did not attend the scheduled appointment, and 2 (22.2%) were delayed due to COVID-19. Of those that had CT scans over the study period (n = 17), 0 scans were normal, one patient (5.9%) had asymptomatic lung cancer, 7 (41.2%) had pulmonary nodules, 11 (64.7%) had emphysema, and 9 (52.9%) had coronary artery disease. ConclusionThis pilot study suggests the feasibility and suggests initial indications of the efficacy of referring ED patients for LCS.

AI-Powered Chest Disease Detection and Classification using Deep Learning

AI-Powered Chest Disease Detection and Classification using Deep Learning Method presents an innovative approach to revolutionize chest disease diagnosis through the integration of advanced deep learning techniques. This initiative focuses on the development of an intelligent system that can effectively detect and classify a variety of chest diseases by analysing medical images, such as chest X-rays and CT scans. At its core, the project involves the compilation and preparation of a diverse dataset containing both normal and disease-affected chest images. Leveraging the capabilities of deep learning, particularly convolutional neural networks (CNNs), the project then designs and trains models to learn intricate patterns and features indicative of different chest diseases. The models are optimized for accuracy and robustness. This efficiency translates to quicker diagnoses, enabling prompt medical interventions. The AI system serves as a complementary tool for healthcare professionals, aiding in decision- making by providing valuable insights and augmenting their expertise.

Analysis of airway structural parameters in Han Chinese adults: a prospective cross-sectional study

Introduction Establishing reference ranges for central airway parameters and exploring their influencing factors in Han Chinese non-smoking adults. Methods This prospective cross-sectional study was conducted on Han Chinese non-smoking adults who underwent chest CT scans at the Tongzhou Campus of Dongzhimen Hospital Affiliated with the Beijing University of Chinese Medicine between September 2022 and November 2022. The SYNAPSE 3D image analysis software was utilized, enabling the extraction of critical parameters such as central airway length, airway wall thickness (AWT), airway lumen area (ALA), and subcarinal angle (SCA). Pearson’s correlation coefficient analysis and multiple linear regression analysis methods were employed to evaluate the relationship between central airway parameters and age, sex, weight, and height. Results The study encompassed 888 Han Chinese non-smoking adults, comprising 456 females and 432 males. Significant sex differences were noted in central airway length, AWT, and ALA, with measurements in males exceeding those in females (p < 0.01) with no significant difference in SCA. Correlation analyses unveiled relationships between central airway parameters and age, sex, weight, and height. During multiple linear regression analyses, no conclusive evidence emerged to demonstrate the independent or combined explanatory or predictive capacity of the aforementioned variables for central airway length and SCA. Although sex has a significant impact on AWT and ALA, its capability in explanation or prediction remains limited. The conclusions drawn from the primary analysis receive reinforcement from the outcomes of sensitivity analyses. Conclusion Establishing the distribution range of central airway parameters in non-smoking Han Chinese adults. It observed significant sex differences in these parameters, except for the SCA. However, the study found that the predictive or explanatory power of age, sex, weight, and height for central airway parameters was either limited or non-significant.

Rare Case of Completely Healed Pneumomediastinum Due to Asthma Exacerbation in A Young Male Patient

Background: The term pneumomediastinum (PNM) refers to the presence of air within the mediastinal cavity. This illness is uncommon but can arise in adolescents with severe asthma attacks. In children aged 5 to 34, the incidence of pneumomediastinum after an acute asthma attack is 1 in 25,000. Men made up the majority of patients (76 percent of all cases). Pneumomediastinum can be diagnosed with the assistance of a chest CT scan. Case: A young man was diagnosed with pneumomediastinum due to an acute asthma attack in this case report. Symptoms of uncontrolled asthma include shortness of breath that worsens with wheezing, chest tightness, and a nonproductive cough. Since the age of 12, the patient in this instance has been receiving salbutamol inhalers. The physical examination revealed polyphonic lung respiration and subcutaneous crepitus in the neck, shoulders, and anterior chest. With adequate management of an asthma episode, pneumomediastinum recovers spontaneously, followed by recurrent symptomatic status, physical examination, and radiography examination. Discussion: Acute asthma exacerbations are one of the factors that can lead to spontaneous pneumomediastinum, in which mediastinal air can permeate the tissue and generate a pneumothorax, and if there is air in the subcutaneous area, it can lead to subcutaneous emphysema. Conclusion: Pneumomediastinum was a rare incidence, pulmonologists examining young adults with acute asthma exacerbations should evaluate for pneumomediastinum. In usual asthma therapy, a chest CT-scan is essential to screen for pneumomediastinum.

Diagnosis and Management for Pulmonary Tuberculoma

Pulmonary tuberculoma, prevalent particularly in tuberculosis (TB)-endemic regions, often appears as a solitary nodule of less than 30 mm, or a tumor of more than 30 mm on radiological examinations. It may also present with a combination of abnormalities, such as multiple nodules with infiltration or pleuritis. Benign solitary pulmonary nodules represent up to 25% of all resected solitary pulmonary nodules, with approximately 5-24% of these identified as pulmonary tuberculoma post-surgery. This condition is prevalent particularly in TB-endemic regions, and must be considered while determining the diagnosis, especially for patients at high risk for lung cancer. Modalities for diagnosing pulmonary tuberculoma include chest radiography, USG, CT scan, PET scan and bronchoscopy with transbronchial biopsy. The treatments for pulmonary tuberculoma are anti-TB drugs and surgery. Pulmonary tuberculoma responds poorly to anti-TB drugs and requires long-term treatment. Surgery is performed when the diameter of tuberculoma still increases after adequate anti-TB treatment.

SURGICAL TREATMENT OF THORACIC AORTA TRANSECTION BY CONTUSIVE TRAUMA: CASE REPORT

Traumatic injuries to the thoracic aorta are rare events with high mortality. Several treatment modalities are described, including: conservative treatment, endovascular techniques, and conventional surgery. The objective of this work is to report the conventional surgical treatment and its evolution of a thoracic aorta transection injury. Case Report: Patient, F.R. 25 years old, victim of blunt motor trauma x shield, was admitted to the unit with chest pain plus signs of shock, a chest CT scan was performed and a grade IV blunt injury to the thoracic aorta and fracture of the right femur were identified, evolving with hypotension, tachycardia and anemia. A posterolateral thoracotomy was performed and, after control and exploration of the hematoma, a lesion with aortic transection was found, with an interposition using a Dacron prosthesis, with a satisfactory clamping time followed by chest drainage. External fixation of the D femur fracture was performed during the same surgery. Red blood cell concentrates were used during the operative period. The patient was admitted to the ICU, on mechanical ventilation, sedated and with amines and blood drains. He developed AKI due to rhabdomyolysis, requiring hemodialysis. A new CT scan showed hemothorax on the right side, and a thoracostomy on the right side was performed on the second day after the first surgery. The patient progressed with weaning from sedation and extubation 5 days after the first surgical procedure, but maintained anemia and mild hypoxia. A new CT scan showed a left hemothorax associated with a small pneumothorax, and a videothoracoscopy was performed on left with evacuation of the clot plus drainage. a good evolution of the condition, with improvement in hematic parameters and renal insufficiency, with gradual removal of the drains. Discharged from hospital without further complications.

Clinical Characteristics of Patients with Hematologic Malignancies and Invasive Aspergillosis: A Cross-sectional Clinical Report from Iran

Background: The incidence of invasive aspergillosis (IA) has significantly increased in recent decades. In patients with hematologic malignancies (HM), IA is associated with higher mortality rates. Objectives: This study aimed to assess the clinical features of IA in patients with HM. Methods: In this retrospective study, we used the hospital information system (HIS) database to extract clinical and paraclinical characteristics of patients with various HMs who received a diagnosis of probable or proven IA during their hospitalization at Imam Khomeini Hospital Complex in Tehran, Iran, between March 2018 and March 2022. Results: After evaluating 350 patients with HM, 51 patients with IA (14.6%) were identified, of whom 40 cases (78.4%) were probable and 11 (21.6%) were proven. Among these, 34 individuals (66.7%) were male. The most common symptoms were fever (n = 23, 62.7%), cough (n = 20, 39.2%), and fever that did not respond to antibiotic therapy (n = 16, 31.4%). The most prevalent malignancies were AML (n = 28, 54.9%), ALL (n = 16, 31.4%), and lymphoma (n = 7, 13.7%). Out of the 51 patients with HM and IA, 48 (94.1%) had abnormal findings on chest CT scans, with the majority (n = 31, 72.1%) showing a nodule with a halo sign. Aspergillus flavus (n = 19/24, 79.2%) was the most commonly isolated species. Initially, patients received liposomal amphotericin B or caspofungin as empiric antifungal therapy, which was then switched to voriconazole once the diagnosis of IA was probable or proven. Eight patients (15.6%) died. Conclusions: Patients with HM presenting with fever and cough should undergo close monitoring for IA. A higher incidence of IA is observed in AML patients, and voriconazole could be considered as antifungal prophylaxis in HM patients. A. flavus is likely the most frequent cause of IA in Iranian patients with HM.