• Korean Journal of Radiology
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• v.21 no.7
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• pp.919-924
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• 2020

Objective: The current study reported a case series to illustrate the early computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) in pediatric patients. Materials and Methods: All pediatric patients who were diagnosed with COVID-19 and who underwent CT scan in Zhongnan Hospital of Wuhan University from January 20, 2020 to February 28, 2020 were included in the current study. Data on clinical and CT features were collected and analyzed. Results: Four children were included in the current study. All of them were asymptomatic throughout the disease course (ranging from 7 days to 15 days), and none of them showed abnormalities in blood cell counts. Familial cluster was the main transmission pattern. Thin-section CT revealed abnormalities in three patients, and one patient did not present with any abnormal CT findings. Unilateral lung involvement was observed in two patients, and one patient showed bilateral lung involvement. In total, five small lesions were identified, including ground-glass opacity (n = 4) and consolidation (n = 1). All lesions had ill-defined margins with peripheral distribution and predilection of lower lobe. Conclusion: Small patches of ground-glass opacity with subpleural distribution and unilateral lung involvement were common findings on CT scans of pediatric patients in the early stage of the disease.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9259-9264
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• 2014

Background: Previous studies concerning the association between the 5,10-methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism with lung cancer in Asian populations have provided inconclusive findings. Aim: A meta-analysis was performed to investigate a more reliable association between MTHFR C677T polymorphism and lung cancer in Asians. Materials and Methods: A comprehensive search was conducted to identify all case-control studies of MTHFR polymorphisms and lung cancer in Asia, using odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of any association. Results: Meta-analysis results suggested that the MTHFR C677T polymorphism contributed to an increased lung cancer risk in Asian populations (for T vs C: OR=1.11, 95%CI=1.0-1.23; for CT vs CC: OR= 1.1, 95%CI= 0.95-1.2 ; for TT+CT vs CC: OR=1.13, 95%CI=1.0-1.30; for TT vs CC: OR=1.25, 95%CI=1.01-1.30; for TT vs CT+CC: OR=1.16, 95%CI=1.0-1.36). Conclusions: MTHFR C677T polymorphism is significantly associated with lung cancer in Asians.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.82-85
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• 2009

Purpose: The purpose of this study was to evaluate the effect of extended CT field of view (FOV) on PET/CT of Standardized uptake value (SUV) when imaging extends beyond the CT FOV. Materials and Methods: CT images were reconstructed at different FOV sizes (500 and 700 mm). Two sets of CT images were reconstructed from the CT projection data by using two FOV sizes. Twenty patients were used in this study. PET images were reconstructed using attenuation maps with 500 mm CT FOV and 700 mm extended CT FOV images. Region of interests (ROIs) drawn on the PET images. In addition, twenty patients' PET images reconstructed by 500 mm CT FOV and 700 mm extended CT FOV were compared with $SUV_{max}$. Results: When using attenuation maps with 700 mm extended CT FOV, the $SUV_{max}$ analysis of liver (p=0.000), lung (p=0.007), mediastinum (p=0.001) were statistically significant. Conclusions: 700 mm extended CT FOV helps to recover the true activity distribution in the PET emission data. In addition, 700 mm extended CT FOV has affected SUV measurement of liver, lung, mediastinum.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.333-341
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• 2011

To compare between calcium scoring CT (CSCT) and Low-dose CT (LDCT) image finding for coronary artery calcification (CAC) in screening of lung disease by MDCT. A total of 61 subjects who retired-workers exposed to inorganic dust were performed LDCT and CSCT by using a MDCT scanner on the same day, after be approved by the institutional review board, and obtaining the written informed consent from all subjects. LDCT images were read for detecting lung diseases as well as CAC by a experienced chest radiologist, then the subjects were divided either the positive group with CAC or the negative group without it. The CSCT was used to quantify and detect the presence of calcification in the coronary artery, and score of CAC calculated by using a Rapidia software (ver 2.8). In all coronary arteries, calcium score of positive group was higher better than that in negative group, especially in the total calcium (13.7 vs. 582.9, p=0.008) and the left anterior descending artery (3.2 vs. 249.0, p=0.006). CAC findings between CSCT and LDCT image were showed excellent agreement in cut-off point 100(K-value=0.80, 95% CI=0.69-0.91) from total calcium score. CAC findings on LDCT images showed the higher relation with CSCT. Therefore, the obstructive coronary artery disease could be predicted by CAC on LDCT images for screening of lung diseases.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.9-18
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• 2010

This paper proposes a deformable registration method using a demon algorithm for aligning the lungs between end-exhale and end-inhale CT scans. The lungs are globally aligned by affine transformation and locally deformed by a demon algorithm. The use of floating gradient force allows a fast convergence in the lung regions with a weak gradient of the reference image. The active-cell-based demon algorithm helps to accelerate the registration process and reduce the probability of deformation folding because it avoids unnecessary computation of the displacement for well-matched lung regions. The performance of the proposed method was evaluated through comparisons of methods that use a reference gradient force or a combined gradient force as well as methods with and without active cells. The results show that the proposed method can accurately register lungs with large deformations and can reduce the processing time considerably.

• Progress in Medical Physics
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• v.26 no.4
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• pp.229-233
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• 2015

We retrospectively reviewed lung cancer patients who were treated with stereotactic ablative radiotherapy (SABR). We investigated the value of response evaluation after treatment by measuring the volume change of tumors on serial chest computed tomography (CT) examinations. The study included 11 consecutive patients with early-stage (T1-T2aN0M0) non-small cell lung cancer (NSCLC) who were treated with SABR. The median dose of SABR was 6,000 cGy (range 5,000~6,400) in five fractions. Sequential follow-up was performed with chest CT scans. Median follow-up time was 28 months. Radiologic measurement was performed on 51 CT scans with a median of 3 CT scans per patient. The median time to partial response ($T_{PR}$) was 3 months and median time to complete remission ($T_{CR}$) was 5 months. Overall response rate was 90.9% (10/11). Five patients had complete remission, five had partial response, and one patient developed progressive disease without response. On follow-up, three patients (27.2%) developed progressive disease after treatment. We evaluated the the response after SABR. Our data also showed the timing of response after SABR.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.176-182
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• 2023

Since the introduction of low-dose computed tomography (CT) screening for patients at high risk of lung cancer, the detection rate of suspicious lung cancer has increased. In addition, there have been many advances in therapeutics targeting oncogenic drivers in non-small cell lung cancer. Therefore, accurate pathological diagnosis of lung cancer, including molecular diagnosis, is increasingly important. This review examines the problems in the pathological diagnosis of suspected lung cancer. For successful pathological diagnosis of lung cancer, clinicians should determine the appropriate modality of the diagnostic procedure, considering individual patient characteristics, CT findings, and the possibility of complications. Furthermore, clinicians should make efforts to obtain a sufficient amount of tissue sample using non- or less-invasive procedures for pathological diagnosis and biomarker analysis.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.19-26
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• 2018

We proposed a numerical calculation of the proportion of necrotic cells in pulmonary segmentation, pulmonary vessel segmentation lung disease site for diagnosis of lung disease from chest CT images. The first step is to separate the lungs and bronchi by applying a three-dimensional labeling technique from a chest CT image and a three-dimensional region growing method. The second step is to divide the pulmonary vessels by applying the rate of change using the first order polynomial regression, perform noise reduction, and divide the final pulmonary vessels. The third step is to find a disease prediction factor in a two-step image and calculate the proportion of necrotic cells.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.702-712
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• 2013

In this paper, we proposed an algorithm that extracts the airway and lung without loss of information in spite of the pulmonary vessel and nodules of the chest wall in the chest CT images. We use a mask image in order to improve the performance and to save processing time of airway and lung segmentation. In the second step, by converting left and right lungs to binary image using the morphological information, we have removed the solitary pulmonary nodule to identify the value of the threshold lung and the chest wall. The last step is to connect the outer shell of the lung with cubic Spline interpolation by adding the perfect pixel and computing the distance of the removed part. Experimental results using Matlab verified that the proposed method could overcome the drawbacks of the conventional methods.