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1. CT-guided pretreatment biopsy diagnosis in patients with thymic epithelial tumours: diagnostic accuracy and risk of seeding [ CLINICAL RADIOLOGY ] - 2024.04.01
2. Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility [ ACADEMIC RADIOLOGY ] - 2024.02.22
3. Automated CT quantification of interstitial lung abnormality and interstitial lung disease according to the Fleischner Society in patients with resectable lung cancer: prognostic significance [ EUROPEAN RADIOLOGY ] - 2023.11.01
4. Sublobar resection in non-small cell lung cancer: patient selection criteria and risk factors for recurrence [ BRITISH JOURNAL OF RADIOLOGY ] - 2023.10.01
5. Recurrence Patterns and Patient Outcomes in Lung Adenocarcinoma Graded Clinical Stage II or Higher: Ground-Glass Opacity?Does It Matter? [ RADIOLOGY ] - 2023.08.22
6. Prognostic Implications of Synchronous Subsolid Nodules in Patients with Resected Subsolid Lung Adenocarcinoma [ RADIOLOGY ] - 2023.07.31
7. Recurrence Patterns and Patient Outcomes in Resected Lung Adenocarcinoma Differ according to Ground-Glass Opacity at CT [ RADIOLOGY ] - 2023.05.01
8. Air embolism in CT-guided transthoracic needle biopsy: emphasis on pulmonary vein injury [ EUROPEAN RADIOLOGY ] - 2022.10.01
9. Characteristics and outcomes of anterior mediastinal cystic lesions diagnosed on chest MRI: implications for management of cystic lesions [ Insights into Imaging ] - 2022.08.17
10. Differences in the prognostic implication of ground-glass opacity on CT according to pathological nodal status in lung cancers treated with lobectomy or pneumonectomy [ EUROPEAN RADIOLOGY ] - 2022.07.01
11. Volume Doubling Times of Pulmonary Metastases in Patients With Bone and Soft-Tissue Sarcomas: Associations With Subsequent New Metastases and Survival After Metastasectomy [ AMERICAN JOURNAL OF ROENTGENOLOGY ] - 2022.04.01
12. Utility of a Deep Learning Algorithm for Detection of Reticular Opacity on Chest Radiography in Patients With Interstitial Lung Disease [ AMERICAN JOURNAL OF ROENTGENOLOGY ] - 2022.04.01
13. Prognosis for Pneumonic-Type Invasive Mucinous Adenocarcinoma in a Single Lobe on CT: Is It Reasonable to Designate It as Clinical T3? [ KOREAN JOURNAL OF RADIOLOGY ] - 2022.03.01
14. Identification of predictors for brain metastasis in newly diagnosed non-small cell lung cancer: a single-center cohort study [ EUROPEAN RADIOLOGY ] - 2022.02.01
15. Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement [ EUROPEAN RADIOLOGY ] - 2022.02.01
16. Learning Curve for CT-Guided Percutaneous Transthoracic Needle Biopsy: Retrospective Evaluation Among 17 Thoracic Imaging Fellows at a Tertiary Referral Hospital [ AMERICAN JOURNAL OF ROENTGENOLOGY ] - 2022.01.01
17. Prognostic performance in lung cancer according to tumor size: Comparison of axial, multiplanar, and 3-dimensional CT measurement to pathological size [ EUROPEAN JOURNAL OF RADIOLOGY ] - 2021.11.01
18. Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules [ EUROPEAN RADIOLOGY ] - 2021.08.01
19. CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma [ EUROPEAN JOURNAL OF RADIOLOGY ] - 2021.06.01
20. Performance of radiomics models for survival prediction in non-small-cell lung cancer: influence of CT slice thickness [ EUROPEAN RADIOLOGY ] - 2021.05.01
21. Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction [ RADIOLOGY ] - 2021.04.01
22. Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination [ RADIOLOGY ] - 2021.04.01
23. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT [ EUROPEAN RADIOLOGY ] - 2020.09.01
24. Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics [ EUROPEAN RADIOLOGY ] - 2020.09.01
25. Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning [ EUROPEAN RADIOLOGY ] - 2020.09.01
26. Volume Doubling Times of Lung Adenocarcinomas: Correlation with Predominant Histologic Subtypes and Prognosis. [ RADIOLOGY ] - 2020.06.01
27. Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings. [ EUROPEAN RADIOLOGY ] - 2020.03.01
28. Application of deep learning?based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy [ EUROPEAN RADIOLOGY ] - 2019.10.01
29. Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer. [ KOREAN JOURNAL OF RADIOLOGY ] - 2019.10.01
30. Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. [ RADIOLOGY ] - 2019.08.01
31. CT Evaluation for Clinical Lung Cancer Staging: Do Multiplanar Measurements Better Reflect Pathologic T-Stage than Axial Measurements? [ KOREAN JOURNAL OF RADIOLOGY ] - 2019.07.01
32. Serial Texture Analyses on ADC Maps for Evaluation of Antiangiogenic Therapy in Rat Breast Cancer [ ANTICANCER RESEARCH ] - 2019.04.15
33. Deep Learning Applications in Chest Radiography and Computed Tomography Current State of the Art [ JOURNAL OF THORACIC IMAGING ] - 2019.03.01
34. CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network [ KOREAN JOURNAL OF RADIOLOGY ] - 2019.02.01
35. Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer [ EUROPEAN RADIOLOGY ] - 2019.02.01
36. Prediction of Treatment Response in Patients with Chronic Obstructive Pulmonary Disease by Determination of Airway Dimensions with Baseline Computed Tomography [ KOREAN JOURNAL OF RADIOLOGY ] - 2019.02.01
37. Low morphometric complexity of emphysematous lesions predicts survival in chronic obstructive pulmonary disease patients [ EUROPEAN RADIOLOGY ] - 2019.01.15
38. Volume doubling time of lung cancer detected in idiopathic interstitial pneumonia: comparison with that in chronic obstructive pulmonary disease [ EUROPEAN RADIOLOGY ] - 2018.04.01
39. Diagnostic performance of CT-guided percutaneous transthoracic core needle biopsy using low tube voltage (100 kVp): comparison with conventional tube voltage (120 kVp) [ ACTA RADIOLOGICA ] - 2018.04.01
40. Prediction of survival by texture-based automated quantitative assessment of regional disease patterns on CT in idiopathic pulmonary fibrosis [ EUROPEAN RADIOLOGY ] - 2018.03.01
41. CT assessment-based direct surgical resection of part-solid nodules with solid component larger than 5 mm without preoperative biopsy: experience at a single tertiary hospital [ EUROPEAN RADIOLOGY ] - 2017.12.01
42. Doubling time of thymic epithelial tumours on CT: correlation with histological subtype [ EUROPEAN RADIOLOGY ] - 2017.10.01
43. Added value of prone CT in the assessment of honeycombing and classification of usual interstitial pneumonia pattern [ EUROPEAN JOURNAL OF RADIOLOGY ] - 2017.06.01
44. Evaluation of postoperative lung volume and perfusion changes by dual-energy computed tomography in patients with lung cancer [ EUROPEAN JOURNAL OF RADIOLOGY ] - 2017.05.01
45. Texture-Based Automated Quantitative Assessment of Regional Patterns on Initial CT in Patients With Idiopathic Pulmonary Fibrosis: Relationship to Decline in Forced Vital Capacity. [ AMERICAN JOURNAL OF ROENTGENOLOGY ] - 2016.11.01
46. Preoperative staging of non-small cell lung cancer: prospective comparison of PET/MR and PET/CT. [ EUROPEAN RADIOLOGY ] - 2016.11.01
47. Optimal threshold of subtraction method for quantification of air-trapping on coregistered CT in COPD patients. [ EUROPEAN RADIOLOGY ] - 2016.07.01

1. 흉부 X-ray 영상에서 인공지능 기반 2등급의료영상검출?진단보조소프트웨어(CXR-01)를 이용한 기흉(Pneumothorax) 판독 결과의 임상적 유효성을 평가하기 위한 후향적, 단일기관, 단일군, 확증 임상시험 - 2022.10.13 ~ 2023.04.29
2. 미만성 간질성 폐질환의 정량화를 위한 이미지 표준화 기술 고도화 및 적용과 간질성 폐이상 진단을 위한 정량화 소프트웨어 적용 및 검증 - 2022.09.01 ~ 2023.02.28
3. 군의료사업 인공지능 소프트웨어 성능검증 - 2021.11.11 ~ 2022.01.31
4. vendor가 다른 CT 이미지를 표준화하는 알고리즘을 폐암 환자의 radiomics 분석에서 검증하는 연구 - 2021.08.02 ~ 2022.05.31
5. (2019, 중견) Radiomics을 위한 이미지 표준화 기술 개발 및 deep learning 기반 폐암 예후예측 모델 개발 - 2021.03.01 ~ 2022.02.28
6. (주관, 1차, 보산진)인공지능을 사용한 폐CT영상분석 소프트웨어의 안전성 및 유효성 연구 - 2021.03.01 ~ 2022.02.28
7. (주관, 1차, 보산진)인공지능을 사용한 폐CT영상분석 소프트웨어의 안전성 및 유효성 연구 - 2020.09.01 ~ 2021.02.28
8. 감염병 환자 Chest X-ray 데이터베이스 구축 및 알고리즘 성능 평가 - 2020.08.01 ~ 2020.12.31
9. 인공지능을 활용한 흉부 CT 영상 표준화 - 2020.03.26 ~ 2023.03.31
10. Radiomics을 위한 이미지 표준화 기술 개발 및 deep learning 기반 폐암 예후예측 모델 개발 - 2020.03.01 ~ 2021.02.28
11. 폐암 고위험 및 폐결절 확진 환자군 대상 인공지능 기반 폐결절 진단보조 시스템 임상검증 및 적용 - 2020.01.01 ~ 2021.12.31
12. Radiomics을 위한 이미지 표준화 기술 개발 및 deep learning 기반 폐암 예후예측 모델 개발 - 2019.09.01 ~ 2020.02.29
13. 흉부 CT 영상을 이용한 인공지능 기반 폐 결절 검출 소프트웨어 AVIEW Lung Nodule CAD의 유효성을 평가하기 위한 단일기관, 후향적 확증 임상시험 - 2019.08.08 ~ 2020.03.31
14. 흉부 CT 영상을 이용한 인공지능 기반 폐질환 정량분석 소프트웨어 AVIEW LungFA의 유효성을 평가하기 위한 단일기관, 단일군, 후향적 확증임상시험 - 2019.03.05 ~ 2020.01.06
15. 폐암에서 Texture 분석과 Deep learning을 이용한 radiomics platform 개발 - 2019.03.01 ~ 2019.05.31
16. 폐암 고위험 및 폐결절 확진 환자군 대상 인공지능 기반 폐결절 진단보조 시스템 임상검증 및 적용 - 2019.01.01 ~ 2019.12.31
17. 폐절결의 분할 및 악성도 예측 모델 구축을 위한 질감분석, 딥 러닝 기반 분석 platform 개발 - 2018.01.01 ~ 2018.12.31
18. 만성 폐혈관색전성 폐동맥 고혈압 환자군에서 경피적 폐동맥풍선확장술의 치료 계획 수립을 위한 예후 예측 인자로서 이중에너지 CT와 관류 SPECT의 관류 차이에 대한 비교 연구 - 2018.01.01 ~ 2018.12.31
19. Texture 분석과 Deep learning을 이용한 radiogenomics platform 개발 - 2016.06.01 ~ 2017.05.31
20. 미만성 폐질환 환자에서 CT의 3D texture를 이용한 자동 정량화 소프트웨어 개발 및 예후 예측을 위한 영상 생체표지자 개발 - 2015.01.01 ~ 2015.12.31
21. 리피오돌을 이용한 간유리음영 폐병변의 위치선정 방법의 안전성과 유효성 평가를 위한 다기관 임상 시험(LOGIS trial) - 2014.05.22 ~ 2017.02.28

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