• Fire Science and Engineering
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• v.22 no.2
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• pp.121-128
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• 2008

Adverse health effects of inhaled smokes are associated with the amount of the particles deposited in human lung. Lung model is needed to simulate smoke deposition because of the hardness of the in vivo deposition experiment. However, it is hard to realize the successively decreasing bifurcations in the model. In this work, an experimental lung model was developed to simulate the smoke deposition in the lung. Instead of bifurcating airways, the lung model was made of packed beds of which size decreased downwards. The experimental results using this model showed good agreements with existing results for real lung in the deposition characteristics. The model could be applied to the studies of health risk assessment of the inhaled smoke particles generated by fire.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.476-488
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• 2021

Objective: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. Materials and Methods: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. Results: The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). Conclusion: The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.

• Journal of Chest Surgery
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• v.30 no.6
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• pp.566-572
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• 1997

A major obstacles to evaluation of newly-developed treatment strategy for human lung cancer has been the lack of appropriate experimental animal models. We describe a new experimental model of orthotopically-developed non-small cell lung cancer in nude rat, involving inoculation of tumor cell suspension by thoracotomy. Over 40 direct implantation to the periphery of the lung has been performed to date, each requiring less than'1 hour for completion. This model has been used to perform a series of experiments to investigate whether the rat lung and surrounding structures trapped tumor cells with 2 different non-small cell lung cancer cell lines(NCI-H46O and NCI-H1299). Every animal showed development of tumor masses, which were loculated at the periphery of the lung karenchyma and identified also by radiography. After 3 weetu of the inoculation, tumor develop meat at the mediastinal strutures were identified. The life expectancies of the victims were different between the cell lines, but were approximately 5 weeks when NCI-H46O cell line was used. This new orthotopic lung cancer model may be facilitate future studies of the new therapeutics of localized non-small cell lung cancer .

• Biomolecules & Therapeutics
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• v.30 no.2
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2819-2822
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• 2012

Objective: To assess induction effects of Chlamydia pneumoniae (Cpn) on lung cancer in rats. Methods: A lung cancer animal model was developed through repeated intratracheal injection of Cpn (TW-183) into the lungs of rats, with or without exposure to benzo(a)pyrene (Bp). Cpn antibodies (Cpn-IgA, -IgG, and -IgM) in serum were measured by microimmunofluorescence. Cpn-DNA or Cpn-Ag of rat lung cancer was detected through polymerase chain reaction or enzyme-linked immunosorbent assay. Results: The prevalence of Cpn infection was 72.9% (35/48) in the Cpn group and 76.7% (33/43) in the Cpn plus benzo(a)pyrene (Bp) group, with incidences of lung carcinomas in the two groups of 14.6% (7/48) and 44.2% (19/43), respectively (P-values 0.001 and <0.000 compared with normal controls). Conclusions: A rat model of lung carcinoma induced by Cpn infection was successfully established in the laboratory for future studies on the treatment, prevention, and mechanisms of the disease.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1759-1764
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• 2015

Purpose: To establish the concept of lung cancer hazard assessment theoretical models, evaluating the degree of lung cancer risk of Beijing for regional population lung cancer hazard assessment to provide a basis for technical support. Materials and Methods: ISO standards were used to classify stratified analysis for the entire population, life cycle, processes and socioeconomic management. Associated risk factors were evaluated as lung cancer hazard risk assessment first class indicators. Study design: Using the above materials, indicators were given the weight coefficients, building lung cancer risk assessment theoretical models. Regional data for Beijing were entered into the theoretical model to calculate the parameters of each indicator and evaluate the degree of local lung cancer risk. Results: Adopting the concept of lung cancer hazard assessment and theoretical models for regional populations, we established a lung cancer hazard risk assessment system, including 2 first indicators, 8 secondary indicators and 18 third indicators. All indicators were given weight coefficients and used as information sources. Score of hazard for lung cancer was 84.4 in Beijing. Conclusions: Comprehensively and systematically building a lung cancer risk assessment theoretical model for regional populations in conceivable, evaluating the degree of lung cancer risk of Beijing, providing technical support and scientific basis for interventions for prevention.

• The Journal of Pediatrics of Korean Medicine
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• v.23 no.3
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• pp.233-240
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• 2009

Objectives To evaluate that Okwada affected which factors for treatment of lung fibrosis. Methods Bleomycin induced lung fibrosis model made in mice. After Okwada lyophilized, power sample obtained and melt in distilled water. Okwada solution administered mice through oral route on 21 days after bleomycin instillation and this procedure performed once a day for 7 days. We divided by three groups; normal (control), bleomycin induced lung fibrosis without treatment (experimental), bleomycin induced lung fibrosis with treatment (treatment). On six weeks after bleomycin instillation, mice sacrificed and removed lung. Weperformed Western blot analysis for TGF-beta, phosphodiesterase 5A, interleukin (4,5,13) and compared therapeutic effects of Okwada. Results On western blot analysis, all normal and experimental mice detected TGF-beta, phosphodiesterase 5A, interleukin 4,5,13. The amount of band of TGF-beta, phosphodiesterase 5A, interleukin 5 in experimental and treatment group was similar. However, interleukin 4,13 of treatment group decreased compared with experimental group. Conclusions Okwada would be effected the lung fibrosis through suppression of interleukin 4,13.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5673-5679
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• 2014

Background: Previous published data on the association between CYP1A2 rs762551, rs2069514, rs2069526, and rs2470890 polymorphisms and lung cancer risk have not allowed a definite conclusion. The present meta-analysis of the literature was performed to derive a more precise estimation of the relationship. Materials and Methods: 8 publications covering 23 studies were selected for this meta-analysis, including 1,665 cases and 2,383 controls for CYP1A2 rs762551 (from 8 studies), 1,456 cases and 1,792 controls for CYP1A2 rs2069514 (from 7 studies), 657 cases and 984 controls for CYP1A2 rs2069526 (from 5 studies) and 691 cases and 968 controls for CYP1A2 rs2470890 (from 3 studies). Results: When all the eligible studies were pooled into the meta-analysis for the CYP1A2 rs762551 polymorphism, significantly increased lung cancer risk was observed in the dominant model (OR=1.21, 95 % CI=1.00-1.46). In the subgroup analysis by ethnicity, significantly increased risk of lung cancer was observed in Caucasians (dominant model: OR=1.29, 95%CI=1.11-1.51; recessive model: OR=1.33, 95%CI=1.01-1.75; additive model: OR=1.49, 95%CI=1.12-1.98). There was no evidence of significant association between lung cancer risk and CYP1A2 rs2069514, s2470890, and rs2069526 polymorphisms. Conclusions: In summary, this meta-analysis indicates that the CYP1A2 rs762551 polymorphism is linked to an increased lung cancer risk in Caucasians. Moreover, our work also points out the importance of new studies for rs2069514 associations in lung cancer, where at least some of the covariates responsible for heterogeneity could be controlled, to obtain a more conclusive understanding about the function of the rs2069514 polymorphism in lung cancer development.