• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1104-1120
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• 2020

The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

• 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 Korean Medical Science
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• v.33 no.53
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• pp.342.1-342.6
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• 2018

We validated the diagnostic performance of a previously developed blood-based 7-protein biomarker panel, $AptoDetect^{TM}$-Lung (Aptamer Sciences Inc., Pohang, Korea) using modified aptamer-based proteomic technology for lung cancer detection. Non-small cell lung cancer (NSCLC), 200 patients and benign nodule controls, 200 participants were enrolled. In a high-risk population corresponding to ${\geq}55years$ of age and ${\geq}30pack-years$, the diagnostic performance was improved, showing 73.3% sensitivity and 90.5% specificity with an area under the curve of 0.88. $AptoDetect^{TM}$-Lung (Aptamer Sciences Inc.) offers the best validated performance to discriminate NSCLC from benign nodule controls in a high-risk population and could play a complementary role in lung cancer screening.

• Journal of Chest Surgery
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• v.15 no.2
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• pp.148-154
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• 1982

The experience with operative treatment for peripheral situated solitary circumscribed lesions of the lung at the Department of Thorac. & Cardiovasc. Surg., Korea University Hospital during 8 years from March 1974, through April, 1982 was reviewed. Our criteria for Solitary pulmonary nodule were 1. Round or Ovoid shape 2. Surrounded by normal lung Parenchyme 3. Well circumscribed peripheral location 4. No other visible pulmonary diseases on chest X-ray except minimal atelectasis or pneumonitis 5. Largest diameter less than 8 cm Of the 55 patients reviewed, there were 69% of malignancy and 31% of benign pulmonary diseases. In malignancy 38 patients, there were 18 patients with squamous cell carcinoma, 8 patients with undifferentiated large cell carcinoma, 2 patients with undifferentiated small cell carcinoma, 10 patients with adenocarcinoma and patient with metastatic carcinoma. In benign pulmonary nodule 17 patients, here were 5 patients with tuberculoma, 5 patients with aspergilloma, 2 patients with A-V fistula, 1 patient with pulmonary blastoma, 1 patient with paragonimiasis, and 1 patient with lung abscess. Overall male to female occurrence ratio was 39:16, and most prevalent age incidence was 7th decades. Most frequent size distribution was 4-6 cm in diameter. All of benign diseases were cured by resection and 66% of malignancy performed operation and has 75% resectability.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.623-632
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• 2009

In this paper, we propose a pulmonary nodule registration for the tracking of lung nodules in sequential CT scans. Our method consists of following five steps. First, a translational mismatch is corrected by aligning the center of optimal bounding volumes including each segmented lung. Second, coronal maximum intensity projection(MIP) images including a rib structure which has the highest intensity region in baseline and follow-up CT series are generated. Third, rigid transformations are optimized by normalized average density differences between coronal MIP images. Forth, corresponding nodule candidates are defined by Euclidean distance measure after rigid registration. Finally, template matching is performed between the nodule template in baseline CT image and the search volume in follow-up CT image for the nodule matching. To evaluate the result of our method, we performed the visual inspection, accuracy and processing time. The experimental results show that nodules in serial CT scans can be rapidly and correctly registered by coronal MIP-based rigid registration and local template matching.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.237-241
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• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• Radiation Oncology Journal
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• v.32 no.3
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• pp.116-124
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• 2014

Purpose: To investigate the frequency and clinical significance of detected incidental lung nodules found on computed tomography (CT) simulation images for hepatocellular carcinoma (HCC) using computer-aided diagnosis (CAD) and a physician review. Materials and Methods: Sixty-seven treatment-$na{\ddot{i}}ve$ HCC patients treated with transcatheter arterial chemoembolization and radiotherapy (RT) were included for the study. Portal phase of simulation CT images was used for CAD analysis and a physician review for lung nodule detection. For automated nodule detection, a commercially available CAD system was used. To assess the performance of lung nodule detection for lung metastasis, the sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were calculated. Results: Forty-six patients had incidental nodules detected by CAD with a total of 109 nodules. Only 20 (18.3%) nodules were considered to be significant nodules by a physician review. The number of significant nodules detected by both of CAD or a physician review was 24 in 9 patients. Lung metastases developed in 11 of 46 patients who had any type of nodule. The sensitivities were 58.3% and 100% based on patient number and on the number of nodules, respectively. The NPVs were 91.4% and 100%, respectively. And the PPVs were 77.8% and 91.7%, respectively. Conclusion: Incidental detection of metastatic nodules was not an uncommon event. From our study, CAD could be applied to CT simulation images allowing for an increase in detection of metastatic nodules.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.37-44
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• 2013

In this paper, we propose the pulmonary nodule detection system based on voxel classification. The proposed system consists of three main steps. In the first step, we segment lung volume. In the second step, the lung structures are initially segmented. In the last step, we classify the nodules using voxel classification. To describe characteristics of each voxel, we extract the log-polar sampling based features. Support Vector Machine is applied to the extracted features to classify into nodules and non-nodules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• Journal of Chest Surgery
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• v.52 no.2
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• pp.119-123
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• 2019

Spontaneous regression of lung cancer is a very rare and poorly understood phenomenon. A 64-year-old man presented to Dong-A University Hospital with a shrunken nodule in the right lower lobe. Although the nodule showed a high likelihood of malignancy on needle aspiration biopsy, the patient refused surgery. The nodule spontaneously regressed completely in the next 17 months. However, the subcarinal lymph node was found to be enlarged 16 months after complete regression was observed. We pathologically confirmed metastasis of squamous cell carcinoma and performed neoadjuvant chemotherapy, surgery, and adjuvant chemoradiation. Regardless of tumor size reduction, it is preferable to perform surgery aggressively in cases of operable lung cancer.