• Tuberculosis and Respiratory Diseases
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• v.57 no.1
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• pp.7-10
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• 2004

• Tuberculosis and Respiratory Diseases
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• v.71 no.6
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• pp.438-444
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• 2011

Background: Lung abscess is necrosis of the pulmonary parenchyma caused by microbial infection. At present, clinical outcomes after treatment are good. However, the pulmonary parenchymal changes on the chest computed tomography (CT) after treatment are not well known. We studied the changes of pulmonary parenchyma on plane chest radiography and chest CT in patients with lung abscess following the administration of antibiotics. Methods: We retrospectively reviewed 39 patients who had lung abscess with or without combined pneumonia from January 2006 to July 2010. We studied the therapeutic response in plane chest radiography of them at 1, 2, or more than 3 months following treatment. If any chest CT of them during the study period, we reviewed. Results: Mean age of the patients was about $61.3{\pm}11.2$. Mean duration of antibiotics administration was about $36.7{\pm}26.8$ days. After 3 months of following plane chest radiography, 10 patients (36%) showed without residual sequelae among 28 patients. Findings from other patients showed decrease in densities (11 patients, 39%), fibrostreaky sequelae (4 patients, 14%) and bullae (3 patients, 10%). After more than 2 months, chest CT was checked only in 7 patients. Among the 7 patients, 4 patients showed no residual lesion, 3 patients showed decreased densities on plane chest radiography. Chest CT revealed fibrostreaky densities in 2 patients, ground glass opacities in 3 patients, bullous formation in 1 patient, and cystic bronchiectasis in 1 patient. Conclusion: After more than 2 months following treatment for lung abscess even though there were no lesions on plane chest radiography, chest CT showed fibrostreaky or ground glass opacity.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.657-662
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• 2014

Background: The epidermal growth factor receptor (EGFR) mutation status of lung cancer is important because it means that EGFR-tyrosine kinase inhibitor treatment is indicated. The purpose of this prospective study is to determine whether EGFR mutation status could be identified with reference to preoperative factors. Materials and Methods: One hundred-forty eight patients with lung cancer (111 adenocarcinomas, 25 squamous cell carcinomas and 12 other cell types) were enrolled in this study. The EGFR mutation status of each lung cancer was analyzed postoperatively. Results: There were 58 patients with mutant EGFR lung cancers (mutant LC) and 90 patients with wild-type EGFR lung cancers (wild-type LC). There were significant differences in gender, smoking status, maximum tumor diameter in chest CT, type of tumor shadow, clinical stage between mutant LC and wild-type LC. EGFR mutations were detected only in adenocarcinomas. Maximum standardized uptake value (SUVmax:$3.66{\pm}4.53$) in positron emission tomography-computed tomography of mutant LC was significantly lower than that ($8.26{\pm}6.11$) of wild-type LC (p<0.0001). Concerning type of tumor shadow, the percentage of mutant LC was 85.7% (6/7) in lung cancers with pure ground glass opacity (GGO), 65.3%(32/49) in lung cancers with mixed GGO and 21.7%(20/92) in lung cancers with solid shadow (p<0.0001). For the results of discriminant analysis, type of tumor shadow (p=0.00036) was most significantly associated with mutant EGFR. Tumor histology (p=0.0028), smoking status (p=0.0051) and maximum diameter of tumor shadow in chest CT (p=0.047) were also significantly associated with mutant EGFR. The accuracy for evaluating EGFR mutation status by discriminant analysis was 77.0% (114/148). Conclusions: Mutant EGFR is significantly associated with lung cancer with pure or mixed GGO, adenocarcinoma, never-smoker, smaller tumor diameter in chest CT. Preoperatively, EGFR mutation status can be identified correctly in about 77 % of lung cancers.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.373-381
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• 2021

In this paper, we propose a classification model based on convolutional neural network(CNN) for predicting 2-year recurrence in non-small cell lung cancer(NSCLC) patients using preoperative chest CT images. Based on the region of interest(ROI) defined as the tumor internal and external area, the input images consist of an intratumoral patch, a peritumoral patch and a peritumoral texture patch focusing on the texture information of the peritumoral patch. Each patch is trained through AlexNet pretrained on ImageNet to explore the usefulness and performance of various patches. Additionally, ensemble learning of network trained with each patch analyzes the performance of different patch combination. Compared with all results, the ensemble model with intratumoral and peritumoral patches achieved the best performance (ACC=98.28%, Sensitivity=100%, NPV=100%).

• Journal of Chest Surgery
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• v.44 no.6
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• pp.455-457
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• 2011

A 70-year-old male visited urgent care due to coughing for 1 month and left chest pain. He had no history of trauma. The initial chest computed tomography (CT) showed the 7th left intercostal lung herniation. A follow-up CT showed an intercostal lung herniation combined with a bowl herniation, which had developed due to a Morgagni's hernia. An emergency operation was performed due to the incarceration of the bowl and lung. The primary repair of the diaphragm was performed and the direct approximation of the 7th intercostal space was determined. We concluded that the defect of the diaphragm and the intercostal muscle was a congenital lesion, and the recurrent coughing was the aggravating factor of herniation.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.454-463
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• 2021

Interstitial lung abnormalities (ILAs) are radiologic abnormalities found incidentally on chest CT that are potentially related to interstitial lung diseases. Several articles have reported that ILAs are associated with increased mortality, and they can show radiologic progression. With the increased recognition of ILAs on CT, the role of radiologists in reporting them is critical. This review aims to discuss the clinical significance and radiologic characteristics of ILAs to facilitate and enhance their management.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.34-50
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• 2023

In 2019, the American College of Radiology announced Lung CT Screening Reporting & Data System (Lung-RADS) 1.1 to reduce lung cancer false positivity compared to that of Lung-RADS 1.0 for effective national lung cancer screening, and in December 2022, announced the new Lung-RADS 1.1, Lung-RADS^Ⓡ 2022 improvement. The Lung-RADS^Ⓡ 2022 measures the nodule size to the first decimal place compared to that of the Lung-RADS 1.0, to category 2 until the juxtapleural nodule size is < 10 mm, increases the size criterion of the ground glass nodule to 30 mm in category 2, and changes categories 4B and 4X to extremely suspicious. The category was divided according to the airway nodules location and shape or wall thickness of atypical pulmonary cysts. Herein, to help radiologists understand the Lung-RADS^Ⓡ 2022, this review will describe its advantages, disadvantages, and future improvements.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.688-700
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• 2020

Purpose To compare the incidence, survival rate, and CT findings of acute exacerbation (AE) of idiopathic pulmonary fibrosis (IPF) between patients with and without lung cancer. Materials and Methods From June 2004 to July 2018, 89 consecutive patients diagnosed with IPF were included. Among them, 26 patients had IPF with lung cancer (IPF-LCA), and 63 patients had IPF alone. The clinical characteristics and CT findings associated with IPF, lung cancer, and AE were reviewed. Surgery and chemotherapy were performed for 6 and 23 cases of lung cancer, respectively, as the first- or second-line anticancer treatment. The overall survival, CT findings, disease-free period before AE, and duration from the onset of AE to death were compared. Results The incidence of AE was 61.5% in the IPF-LCA group and 58.7% in the IPF group (p = 0.806). The mean overall survival in the IPF-LCA and IPF groups were 16.8 and 83.0 months, respectively (p < 0.001). The mean durations from the start of the lung cancer treatment to the onset of AE were 16.0 and 4.6 months in cases of surgical treatment and chemotherapy, respectively. In comparison of death from AE, the survival rate was significantly lower in the IPF-LCA group than in the IPF group (p = 0.008). In the CT findings associated with AE, the IPF-LCA group tended to have a peribronchial (p < 0.001) or asymmetric distribution (p = 0.016). Conclusion In patients with IPF who develop lung cancer, the rate of death from AE is higher than that in patients with IPF alone. They tend to have unusual CT patterns associated with AE, such as a peribronchial or asymmetric distribution.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.6
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• pp.488-490
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• 2008

A 75-year-old man with non-small cell lung cancer (NSCLC) underwent F-18 fluorodeoxyglucose (FDG) PET/CT for staging. PET/CT showed distant metastases to intra-abdominal lymph nodes (LNs) as well as bilateral mediastinal LNs (stage IV), He underwent PET/CT (restaging PET/CT) 1week after the completion of first-line chemotherapy (docetaxel+carboplatin). It showed markedly increased FDG uptake in primary tumor, whereas tumor size decreased significantly, compared to prior PET/CT. This lesion was aggravated on follow-up CT 3 months after the completion of chemotherapy. Although there are several reports that FDG PET has potential to evaluate early response to chemotherapy and prognosis, there are a few cases to show mismatch between FDG uptake and size on PET/CT. Thus we report a case of NSCLC showed increased FDG uptake of primary tumor while decreased tumor size on restaging PET/CT.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.