• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2691-2696
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• 2014

SNOMED CT is a standard clinical terminology to provide a standardized way to record and manage clinical records for EMR or EHR. The structure of SNOMED CT is very complex because of huge expressive power and its internal design mechanism. Although there are some SNOMED CT browsers for browsing and searching SNOMED CT concepts, they are less effective because they do not consider such complexity of SNOMED CT. This paper depicts problems of current SNOMED CT browsers and analyze SNOMED CT dataset. Then, this paper analyze requirements of SNOMED CT browser which improves searching and selecting of appropriate clinical terms.

• The Journal of Korean Academic Society of Nursing Education
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• v.22 no.3
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• pp.304-315
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• 2016

Purpose: The purpose of this study was to explore the relationships of critical thinking disposition (CT), nursing work environment (NWE), and clinical decision making ability among nurses. Methods: A cross-sectional, descriptive study design was conducted on 192 nurses who had worked for more than six months in five general hospitals. A self-reported questionnaire was used to collect data, which included demographics, CT, NWE, and clinical decision making ability. Results: The mean score of CT was 3.5. The highest score was on the objectivity of CT and the lowest on systematicity. The mean score of NWE was 2.3. The highest score was on the collegial nurse-physician relations of NWE and the lowest on the staffing and resource adequacy. The mean score of clinical decision making ability was 3.3. In hierarchical multiple regression, affecting factors on clinical decision making ability were CT and NWE. Conclusion: The findings showed that clinical decision making ability is associated with CT and NWE. To improve clinical decision making ability, it is important to improve CT. In addition, it should be considered to improve NWE where the nurses can make a decision with their job through critical thinking.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1749-1763
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• 2021

Coronary computed tomography angiography (CCTA) is routinely used for anatomical assessment of coronary artery disease (CAD). However, invasive measurement of fractional flow reserve (FFR) is the current gold standard for the diagnosis of hemodynamically significant CAD. CT-derived FFRCT and CT perfusion are two emerging techniques that can provide a functional assessment of CAD for risk stratification and clinical decision making. Several clinical studies have shown that the diagnostic performance of concomitant CCTA and functional CT assessment for detecting hemodynamically significant CAD is at least non-inferior to that of other routinely used imaging modalities. This article aims to review the current clinical evidence and recent developments in functional CT techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.416-417
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• 2014

SNOMED CT is a standard clinical terminology to provide a standardized way for recording and managing of clinical records in EMR systems. However, because of its huge expressive power, it is very difficult to consistently record patients' status such as diagnosis and procedure. The reason is that one clinical meaning can be expressed in variety of ways using multiple terminologies and vice versa. This paper proposes a novel requirement of effective search browser for SNOMED CT terminologies by analyzing duplicated or similar terminologies.

• Korean Journal of Clinical Laboratory Science
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• v.36 no.2
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• pp.193-198
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• 2004

PET/CT combines the functional information from a positron emission tomography (PET) exam with the anatomical information from a computed tomography (CT) exam into one single exam. A CT scan uses a combination of x-rays and computers to give the radiologist a non-invasive way to see inside your body. One advantage of CT is its ability to rapidly acquire two-dimensional pictures of your anatomy. Using a computer these 2-D images can be presented in 3-D for in-depth clinical evaluation. A PET scan detects changes in the cellular function - how your cells are utilizing nutrients like sugar and oxygen. Since these functional changes take place before physical changes occur, PET can provide information that enables your physician to make an early diagnosis. The PET exam pinpoints metabolic activity in cells and the CT exam provides an anatomical reference. When these two scans are fused together, your physician can view metabolic changes in the proper anatomical context of your body. PET/CT offers significant advantages including more accurate localization of functional abnormalities, and the distinction of pathological from normal physiological uptake, and improvements in monitoring treatment. A PET/CT scan allows physicians to measure the body's abnormal molecular cell activity to detect cancer (such as breast cancer, lung cancer, colorectal cancer, lymphoma, melanoma and other skin cancers), brain disorders (such as Alzheimer's disease, Parkinson's disease, and epilepsy), and heart disease (such as coronary artery disease).

• Progress in Medical Physics
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• v.32 no.1
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• pp.1-17
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• 2021

The evolution of X-ray computed tomography (CT) has been based on the discovery of X-rays, the inception of the Radon transform, and the development of X-ray digital data acquisition systems and computer technology. Unlike conventional X-ray imaging (general radiography), CT reconstructs cross-sectional anatomical images of the internal structures according to X-ray attenuation coefficients (approximate tissue density) for almost every region in the body. This article reviews the essential physical principles and technical aspects of the CT scanner, including several notable evolutions in CT technology that resulted in the emergence of helical, multidetector, cone beam, portable, dual-energy, and phase-contrast CT, in integrated imaging modalities, such as positron-emission-tomography-CT and single-photon-emission-computed-tomography-CT, and in clinical applications, including image acquisition parameters, CT angiography, image adjustment, versatile image visualizations, volumetric/surface rendering on a computer workstation, radiation treatment planning, and target localization in radiotherapy. The understanding of CT characteristics will provide more effective and accurate patient care in the fields of diagnostics and radiotherapy, and can lead to the improvement of image quality and the optimization of exposure doses.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.3
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• pp.253-255
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• 2008

A 61-year-old man had a F-18 FDG PET/CT scan for evaluation of a common bile duct cancer identified on CT. The PET/CT image showed a hypermetabolic mass in the common bile duct, and a focal area of increased F-18 FDG uptake in segment IV of the liver, which corresponded to a hypoattenuated lesion on non-enhanced CT, and was consistent with hepatic metastasis. The patient underwent choledochojejunostomy with hepatic resection, and pathologic findings were compatible with an eosinophilic abscess in the liver. This case demonstrates that F-18 FDG uptake by an eosinophilic abscess can mimic hepatic metastasis in a patient with a malignancy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.323-325
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• 2014

SNOMED CT is a standard clinical terminology which is to efficiently record, manage and utilize clinical records during clinical processes. However, huge expressive power of SNOMED CT makes it difficult to select appropriate terms during short consultation hours. In addition, true meaning of a written record using the terminology may be misunderstood or even distorted since one clinical meaning could be expressed in a variety of ways. This paper analyzes such known problems in a database point of view, and presents effective selection methods of similar terminologies to mitigate the problem.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.921-930
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• 2022

Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on ¹⁸F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. Materials and Methods: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. Results: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. Conclusion: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.