• Tuberculosis and Respiratory Diseases
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• 제53권1호
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• pp.56-65
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• 2002

국내에서 폐 유상피 혈관내피종이 악성으로 진행하여 사망한 예는 1예만 보고되어 있다. 단일 폐결절이 있는 환자에서 개흉 폐절제술후 병리조직학적으로 종양세포의 유사분열과 괴사를 동반하여 악성 유상피 혈관내피종으로 진단하고 예후가 불량할 것으로 예상되는 1예와 단순 흉부 촬영상 양측 다발성 폐결절이 있는 무증상 환자에서 비디오 흉강경 폐생검으로 폐 유상피 혈관내피종을 진단 후 조직학적 및 임상적으로 악성 소견 없이 경과를 관찰 중인 1예 및 양측 폐의 다발성 결절성 병변과 늑막액을 동반하여 개흉 폐생검으로 폐 유상피 혈관내피종을 진단 후 호흡부전으로 사망한 1예의 폐 유상피 혈관내피종 3예를 경험하였기에 문헌 고찰과 함께 보고하는 바이다.

• Korean Journal of Radiology
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• 제22권2호
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• pp.263-280
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• 2021

Percutaneous transthoracic needle biopsy (PTNB) is one of the essential diagnostic procedures for pulmonary lesions. Its role is increasing in the era of CT screening for lung cancer and precision medicine. The Korean Society of Thoracic Radiology developed the first evidence-based clinical guideline for PTNB in Korea by adapting pre-existing guidelines. The guideline provides 39 recommendations for the following four main domains of 12 key questions: the indications for PTNB, pre-procedural evaluation, procedural technique of PTNB and its accuracy, and management of post-biopsy complications. We hope that these recommendations can improve the diagnostic accuracy and safety of PTNB in clinical practice and promote standardization of the procedure nationwide.

• Korean Journal of Radiology
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• 제22권1호
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.