• Journal of Chest Surgery
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• v.23 no.3
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• pp.561-565
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• 1990

We experienced thymolipoma which is a rare benign mediastinal tumor, an admixture of atrophic thymic tissue and matured adipose tissue. The patient was, 13 years old boy, admitted due to anterior mediastinal mass on routine chest X-ray and complained of mild exertional dyspnea. The physical examination revealed percussion dullness, decreased breathing sound at right anterior hemithorax. The chest PA and lateral views showed both paracardiac mass density and obliteration of retrosternal clear space. The chest CT Scan showed tubular structure containing of thymic tissue and enhanced fatty tissue, suggested thymolipoma. Operation was performed under the impression of thymolipoma. Through median sternotomy, the huge mass was resected. Grossly, the mass was yellowish fatty colored, measured 15X12X10cm, weighed 600gm. Light microscopic examination showed that thymic tissue containing of Hassal`s corpuscles were surrounded by matured adipose tissue, as the characteristic finding of thymolipoma. The postoperative course was uneventful.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.285-295
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• 2023

With the development of technology, efforts to reduce the exposure dose received by patients in CT scans are continuing with the development of new reconstruction techniques. Recently, deep learning reconstruction techniques have been developed to overcome the limitations of repetitive reconstruction techniques. This study aims to evaluate the usefulness of images according to reconstruction techniques in pediatric chest CT images. Patient study conducted a study on 85 pediatric patients who underwent chest CT scan at P-Hospital in Gyeongsangnam-do from January 1, 2021 to December 31, 2022. The phantom used in the Phantom Study is the Pediatrics Whole Body Phantom PBU-70. After the test, the images were reconstructed with FBP, ASIR-V (50%) and DLIR (TF-Medium, High), and the images were evaluated by obtaining SNR and CNR values by setting ROI of the same size. As a result, TF-H of deep learning reconstruction techniques had the lowest noise value compared to ASIR-V (50%) and TF-M in all experiments, and SNR and CNR had the highest values. In pediatric chest CT scans, TF images with deep learning reconstruction techniques were less noisy than ASiR-V images with adaptive statistical iterative reconstruction techniques, CNR and SNR were higher, and the quality of images was improved compared to conventional reconstruction techniques.

• Korean Journal of Radiology
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• v.1 no.3
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• pp.135-141
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• 2000

Objective: To compare the clinical utility of the different imaging techniques used for the evaluation of tracheobronchial diseases. Materials and Methods: Forty-one patients with tracheobronchial diseases [tuberculosis (n = 18), bronchogenic carcinoma (n = 10), congenital abnormality (n = 3), post-operative stenosis (n = 2), and others (n = 8)] underwent chest radiography and spiral CT. Two sets of scan data were obtained: one from routine thick-section axial images and the other from thin-section axial images. Multiplanar reconstruction (MPR) and shaded surface display (SSD) images were obtained from thin-section data. Applying a 5-point scale, two observers compared chest radiography, routine CT, thin-section spiral CT, MPR and SSD imaging with regard to the detection, localization, extent, and characterization of a lesion, information on its relationship with adjacent structures, and overall information. Results: SSD images were the most informative with regard to the detection (3.95±0.31), localization (3.95±0.22) and extent of a lesion (3.85±0.42), and overall information (3.83±0.44), while thin-section spiral CT scans provided most information regarding its relationship with adjacent structures (3.56±0.50) and characterization of the lesion (3.51±0.61). Conclusion: SSD images and thin-section spiral CT scans can provide valuable information for the evaluation of tracheobronchial disease.

• Journal of Yeungnam Medical Science
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• v.33 no.1
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• pp.72-75
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• 2016

Pulmonary epithelioid hemangioendothelioma (PEH) is a rare, low-to-intermediate malignant tumor of endothelial origin. Computed tomography (CT) findings of PEH demonstrate multiple small bilateral nodules; however, to the best of our knowledge, there were no reports on PEH coexisting with other malignancies. Here, we reported on a case involving PEH in a patient with colon cancer and breast cancer which was misconceived as pulmonary meta- stasis. A 63-year-old woman who suffered from constipation for 2 weeks visited our hospital. Colonoscopy showed a large mass with obstruction on hepatic flexure. The histological diagnosis was adenocarcinoma of the ascending colon. Multiple nodules in both lungs and breast were observed on a chest CT scan. A core biopsy of a breast nodule was performed and a diagnosis of invasive ductal carcinoma of the left breast was made. Pulmonary nodules observed on the chest CT scan was considered as pulmonary metastasis from colon or breast cancer. Laparoscopic right hemicolectomy was performed. At the same time, wedge resection of the lung was performed and pathological diagnosis was PEH. Radiologic features of PEH were difficult to distinguish from lung metastasis. Therefore the author reported a rare case involving PEH in a patient with primary malignancy of colon and breast.

• Tuberculosis and Respiratory Diseases
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• v.57 no.4
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• pp.381-385
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• 2004

Pulmonary gangrene is a rare and severe complication of bacterial pneumonia, where a pulmonary segment or lobe is sloughed due to parenchymal devitalization of the parenchyma, with secondary anaerobic infection and necrosis caused by pulmonary vascular thrombosis. Prior to the antibiotic era, massive pulmonary gangrene was potentially fatal. Herein, a case of pulmonary gangrene in a 67-year-old man is reported. He complained of fever, chills, dyspnea and purulent sputum of 5 days duration. The plain chest radiograph showed well-marginated right upper lobe consolidation, with bulging minor fissure, suggestive of a Klebsiella infection. A contrast CT scan demonstrated consolidation of the right upper lobe, with a central necrotizing portion. Klebsiella species was confirmed from both sputum and blood cultures. After appropriate antibiotics, the chest X-ray and CT scan 3 weeks later showed a large cavity with an air-fluid level, sloughing-off and extrusion of necrotic lung tissue, suggestive of pulmonary gangrene. Seven months later, the right gangrenous lung showed severe volume loss on a chest radiograph. The management of pulmonary gangrene has been somewhat controversial. Herein, it was managed without surgical drainage or resection. If the antibiotic therapy had failed, then a surgical approach would have been considered.

• The Journal of Churna Manual Medicine for Spine and Nerves
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• v.10 no.2
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• pp.27-35
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• 2015

Objectives: The purpose of this study is to discriminate the cough-induced rib fracture with chest pain occurred after chuna therapy. Methods: A 68-year-old female patient who is suffering from left chest wall pain with chronic cough was treated by Korean medical treatment and chuna therapy from November 11th 2014 to November 15th 2014. The improvement of the patient's pain was measured by 100 mm Visual Analog Scale (VAS). For diagnosis the rib fracture, we conducted the radiography, computed tomography (CT), and bone scan. Results: After treatment, pain intensity was decreased and the rib fracture was negative in radiography and CT. But, in bone scan, the 5th-8th rib fractures in left side were detected. Conclusions: When the patient with chest pain visit the hospital after chuna therapy, a doctor keep in mind the possibility of coughinduced rib fracture.

• Tuberculosis and Respiratory Diseases
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• v.66 no.3
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• pp.236-240
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• 2009

Miliary tuberculosis is quite a rare but serious cause of acute respiratory distress syndrome (ARDS). Therefore, the early detection of military tuberculosis as the underlying cause of ARDS is very important for the prognosis and survival of the patient. We report a case of military tuberculosis mimicking ARDS. A female patient was admitted due to repeated fever and dyspnea. The initial chest CT scan showed diffuse ground glass opacity, without a miliary pattern. The case was considered to be ARDS caused by pneumonia. She showed improvement after being treated with levofloxacin. However, she was re-admitted with fever seven days after discharge. The follow up chest CT scan showed micronodules in both lungs. An open lung biopsy confirmed the diagnosis of military tuberculosis.

• Journal of Chest Surgery
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• v.51 no.3
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• pp.216-219
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• 2018

We report the case of a 16-year-old male patient who was involved in a traffic accident and transferred to the emergency department with mild chest pain. We initially did not find evidence of tracheal injury on computed tomography (CT). Within an hour after presentation, the patient developed severe dyspnea and newly developed subcutaneous emphysema and pneumoperitoneum were discovered. Abdominal CT showed no intra-abdominal injury. However, destruction of the right main bronchus was identified on coronal images of the initially performed CT scan. Emergency exploratory surgery was performed. The amputated right main bronchus was identified. End-to-end tracheobronchial anastomosis was performed, and the patient recovered without any complications.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.714-726
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• 2024

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.