• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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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.

• 대한영상의학회지
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• 제84권1호
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• pp.34-50
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• 2023

미국방사선의학회는 효과적인 국가 폐암 검진 시행을 위해 2019년도에 Lung CT Screening Reporting & Data System (이하 Lung-RADS) 1.0보다 폐암의 위양성을 줄이기 위해 개편된 Lung-RADS 1.1을 발표하였고, 2022년 12월에 새로운 Lung-RADS 1.1 개선안 Lung-RADS^Ⓡ 2022를 발표하였다. Lung-RADS^Ⓡ 2022은 Lung-RADS 1.0과 비교했을 때 결절의 크기는 소수점 첫째 자리까지 측정하고, 늑막근처 결절의 크기가 10 mm 미만인 경우까지 범주 2로 하며, 범주 2에서 간유리 결절의 크기 기준을 30 mm로 높이고, 범주 4B와 4X는 매우 의심으로 변경하며, 기도 결절의 위치와 비정형 폐 낭종의 형태와 벽 두께에 따라 범위를 나누었다. 이에 영상의학과 의사들의 개선된 Lung-RADS^Ⓡ 2022에 대한 이해를 돕고자, 이 종설에서는 Lung-RADS^Ⓡ 2022의 장점, 단점, 그리고 향후 개선점에 대해서 기술하고자 한다.

• Journal of Korean Medical Science
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• 제33권53호
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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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• 제15권2호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권8호
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• pp.623-632
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• 2009

본 논문에서는 연속시점에서 촬영한 CT 영상에서 대응되는 폐결절을 추적 관찰하기 위한 폐결절 정합 방법을 제안한다. 제안 방법은 다음과 같은 다섯 단계로 구생된다. 첫째, 분할된 폐를 포함하는 최적경계볼륨의 중심으로 위치 차이를 보정한다. 둘째, 초기 CT 영상과 추적 CT 영상에서 가장 높은 밝기값을 가지고 있는 갈비뼈 구조를 포함하는 관상최대강도투사 영상을 생성한다. 셋째, 두 관상최대강도투사 영상 간의 정규화된 평균 밝기값 차이를 통해 강체 변환을 최적화한다. 넷째, 강체 정합 후에 폐결절 중심 간의 유클라디안 거리 측정을 통해 대응되는 폐결절 대응 후보를 정의한다. 마지막으로, 폐결절을 매칭하기 위하여 초기 CT 영상 내에 폐결절 템플릿과 추적 CT 영상 내에 탐색 볼륨 간의 템플릿 매칭을 수행 한다. 본 제안 방법의 결과를 평가하기 위하여 육안 평가, 정확성 및 수행시간 측정을 수행하였다. 실험결과 관상최대강도투사를 기반으로 하는 강체정합과 지역적 템플릿 매칭을 이용하여 폐결절이 정확하고 빠르게 정합됨을 알 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제38권5호
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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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• 제32권3호
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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.

• 한국정보전자통신기술학회논문지
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• 제6권1호
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• pp.37-44
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• 2013

본 논문에서는 voxel classification을 이용한 폐 결절 자동 검출 시스템을 제안한다. 제안하는 폐 영상 분석 방법은 크게 세 단계로 구성된다. 첫 번째 단계에서는 분석 대상 폐 영역을 분할한다. 그리고 두 번째 단계는 분할된 폐 영역 내에서 폐 구조물을 분할한다. 마지막으로 두 번째 과정에서 분할된 폐결절후보와 폐혈관 voxel을 대상으로 log-polar sampling을 이용한 특징 벡터를 만들고, 특징벡터를 입력 값으로 하여 support vector machine classifier를 이용하여 분석대상 voxel을 폐 결절 voxel과 비결절 voxel로 구분하여 폐 결절을 검출한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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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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• 제52권2호
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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.