• 대한임상독성학회지
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• 제16권2호
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• pp.61-67
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• 2018

Purpose: To evaluate the association between neutrophil-to-lymphocyte ratio (NLR) and occurrence of aspiration pneumonia in drug intoxication (DI) patients in the emergency department (ED) and to evaluate the relationship between NLR and length of hospital admission/intensive care unit (ICU) admission Methods: A total of 466 patients diagnosed with DI in the ED from January 2016 to December 2017 were included in the analysis. The clinical and laboratory results, including NLR, were evaluated as variables. NLR was calculated as the absolute neutrophil count/absolute lymphocyte count. To evaluate the prognosis of DI, data on the development of aspiration pneumonia were obtained. Also, we evaluated the relationship between NLR and length of hospital admission and between NLR and length of ICU admission. Statistically, multivariate logistic regression analyses, receiver-operating characteristic (ROC) curve analysis, and Pearson's correlation (${\rho}$) were performed. Results: Among the 466 DI patients, 86 (18.5%) developed aspiration pneumonia. Multivariate logistic regression analysis revealed NLR as an independent factor in predicting aspiration pneumonia (odds ratio, 1.7; p=0.001). NLR showed excellent predictive performance for aspiration pneumonia (areas under the ROC curves, 0.815; cut-off value, 3.47; p<0.001) with a sensitivity of 86.0% and a specificity of 72.6%. No correlations between NLR and length of hospital admission (${\rho}=0.195$) and between NLR and length of ICU admission (${\rho}=0.092$) were observed. Conclusion: The NLR is a simple and effective marker for predicting the occurrence of aspiration pneumonia in DI patients. Emergency physicians should be alert for aspiration pneumonia in DI patients with high NLR value (>3.47).

• 대한원격탐사학회지
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• 제36권6_1호
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• pp.1367-1377
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• 2020

본 연구는 2016년 발생한 9.12 경주지진을 중심으로 경주시 건축물의 지진 취약성을 평가하고 지도를 제작하는데 목적이 있다. 지진 취약성을 평가하기위해 지질공학, 물리, 구조적 요인과 관련된 11개의 영향인자를 선정하였으며, 이는 독립변수로 적용되었다. 종속변수로는 9.12 경주지진 당시 실제 피해 입은 건축물의 위치자료가 사용되었다. 평가 모델은 기계학습 방법의 RF와 SVM을 기반으로 구축하였으며, 훈련 및 검증 데이터셋은 70:30 비율로 무작위 선별되었다. 정확도 검증은 ROC 곡선을 사용하여 최적 모델을 선별하였으며, 각 모델의 정확도는 RF(1.000), SVM(0.998), 예측 정확도는 RF(0.947), SVM(0.926) 로 나타났다. RF 모델을 기반으로 경주시 전체 건축물의 예측 값을 도출하였으며, 이를 등급화 하여 지진 취약성 지도를 작성하였다. 행정동별 건물 등급 분포를 살펴본 결과, 황남동, 월성동, 선도동, 내남면이 취약성이 높은 지역으로, 양북면, 강동면, 양남면, 감포읍이 상대적으로 안전한 지역으로 나타났다.

• 대한치과교정학회지
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• 제52권1호
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• pp.3-19
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• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

• Nutrition Research and Practice
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• 제16권5호
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• pp.604-615
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• 2022

BACKGROUND/OBJECTIVES: This study aimed to investigate cancer-specific survival (CSS) and associated risk factors in elderly gastric cancer (EGC) patients. SUBJECTS/METHODS: EGC patients (≥ 70 yrs) who underwent curative gastrectomy between January 2013 and December 2017 at our hospital were included. Clinicopathologic characteristics and survival data were collected. Receiver operating characteristic (ROC) analysis was used to extract the best cutoff point for body mass index (BMI). A Cox proportional hazards model was used to determine the risk factors for CSS. RESULTS: In total, 290 EGC patients were included, with a median age of 74.7 yrs. The median follow-up time was 31 (1-77) mon. The postoperative 1-yr, 3-yr and 5-yr CSS rates were 93.7%, 75.9% and 65.1%, respectively. Univariate analysis revealed risk factors for CSS, including age (hazard ratio [HR] = 1.08; 95% confidence interval [CI], 1.01-1.15), intensive care unit (ICU) admission (HR = 1.73; 95% CI, 1.08-2.79), nutritional risk screening (NRS 2002) score ≥ 5 (HR = 2.33; 95% CI, 1.49-3.75), and preoperative prognostic nutrition index score < 45 (HR = 2.06; 95% CI, 1.27-3.33). The ROC curve showed that the best BMI cutoff value was 20.6 kg/m². Multivariate analysis indicated that a BMI ≤ 20.6 kg/m² (HR = 2.30; 95% CI, 1.36-3.87), ICU admission (HR = 1.97; 95% CI, 1.17-3.30) and TNM stage (stage II: HR = 5.56; 95% CI, 1.59-19.43; stage III: HR = 16.20; 95% CI, 4.99-52.59) were significantly associated with CSS. CONCLUSIONS: Low BMI (≤ 20.6 kg/m²), ICU admission and advanced pathological TNM stages (II and III) are independent risk factors for CSS in EGC patients after curative gastrectomy. Nutrition support, better perioperative management and early diagnosis would be helpful for better survival.

• Korean Journal of Radiology
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• 제22권6호
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• pp.959-969
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• 2021

Objective: This study aimed to evaluate the role of preoperative two-dimensional (2D) shear wave elastography (SWE) in assessing the stages of liver fibrosis in patients with suspected biliary atresia (BA) and compared its diagnostic performance with those of serum fibrosis biomarkers. Materials and Methods: This study was approved by the ethical committee, and written informed parental consent was obtained. Two hundred and sixteen patients were prospectively enrolled between January 2012 and October 2018. The 2D SWE measurements of 69 patients have been previously reported. 2D SWE measurements, serum fibrosis biomarkers, including fibrotic markers and biochemical test results, and liver histology parameters were obtained. 2D SWE values, serum biomarkers including, aspartate aminotransferase to platelet ratio index (APRi), and other serum fibrotic markers were correlated with the stages of liver fibrosis by METAVIR. Receiver operating characteristic (ROC) curves and area under the ROC (AUROC) curve analyses were used. Results: The correlation coefficient of 2D SWE value in correlation with the stages of liver fibrosis was 0.789 (p < 0.001). The cut-off values of 2D SWE were calculated as 9.1 kPa for F1, 11.6 kPa for F2, 13.0 kPa for F3, and 15.7 kPa for F4. The AUROCs of 2D SWE in the determination of the stages of liver fibrosis ranged from 0.869 to 0.941. The sensitivity and negative predictive value of 2D SWE in the diagnosis of ≥ F3 was 93.4% and 96.0%, respectively. The diagnostic performance of 2D SWE was superior to that of APRi and other serum fibrotic markers in predicting severe fibrosis and cirrhosis (all p < 0.005) and other serum biomarkers. Multivariate analysis showed that the 2D SWE value was the only statistically significant parameter for predicting liver fibrosis. Conclusion: 2D SWE is a more effective non-invasive tool for predicting the stage of liver fibrosis in patients with suspected BA, compared with serum fibrosis biomarkers.

• Korean Journal of Radiology
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• 제22권5호
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• pp.770-781
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• 2021

Objective: Chemical exchange-dependent saturation transfer (CEST) MRI is sensitive for detecting solid-like proteins and may detect changes in the levels of mobile proteins and peptides in tissues. The objective of this study was to evaluate the characteristics of chemical exchange proton pools using the CEST MRI technique in patients with dementia. Materials and Methods: Our institutional review board approved this cross-sectional prospective study and informed consent was obtained from all participants. This study included 41 subjects (19 with dementia and 22 without dementia). Complete CEST data of the brain were obtained using a three-dimensional gradient and spin-echo sequence to map CEST indices, such as amide, amine, hydroxyl, and magnetization transfer ratio asymmetry (MTR_asym) values, using six-pool Lorentzian fitting. Statistical analyses of CEST indices were performed to evaluate group comparisons, their correlations with gray matter volume (GMV) and Mini-Mental State Examination (MMSE) scores, and receiver operating characteristic (ROC) curves. Results: Amine signals (0.029 for non-dementia, 0.046 for dementia, p = 0.011 at hippocampus) and MTR_asym values at 3 ppm (0.748 for non-dementia, 1.138 for dementia, p = 0.022 at hippocampus), and 3.5 ppm (0.463 for non-dementia, 0.875 for dementia, p = 0.029 at hippocampus) were significantly higher in the dementia group than in the non-dementia group. Most CEST indices were not significantly correlated with GMV; however, except amide, most indices were significantly correlated with the MMSE scores. The classification power of most CEST indices was lower than that of GMV but adding one of the CEST indices in GMV improved the classification between the subject groups. The largest improvement was seen in the MTR_asym values at 2 ppm in the anterior cingulate (area under the ROC curve = 0.981), with a sensitivity of 100 and a specificity of 90.91. Conclusion: CEST MRI potentially allows noninvasive image alterations in the Alzheimer's disease brain without injecting isotopes for monitoring different disease states and may provide a new imaging biomarker in the future.

• Korean Journal of Radiology
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• 제22권1호
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• pp.106-117
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• 2021

Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

• Korean Journal of Radiology
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• 제23권12호
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• pp.1260-1268
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• 2022

Objective: To propose standardized MRI-proton density fat fraction (PDFF) cutoff values for diagnosing hepatic steatosis, evaluated using contemporary PDFF measuring methods in a large population of healthy adults, using histologic fat fraction (HFF) as the reference standard. Materials and Methods: A retrospective search of electronic medical records between 2015 and 2018 identified 1063 adult donor candidates for liver transplantation who had undergone liver MRI and liver biopsy within a 7-day interval. Patients with a history of liver disease or significant alcohol consumption were excluded. Chemical shift imaging-based MRI (CS-MRI) PDFF and high-speed T2-corrected multi-echo MR spectroscopy (HISTO-MRS) PDFF data were obtained. By temporal splitting, the total population was divided into development and validation sets. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of the MRI-PDFF method. Two cutoff values with sensitivity > 90% and specificity > 90% were selected to rule-out and rule-in, respectively, hepatic steatosis with reference to HFF ≥ 5% in the development set. The diagnostic performance was assessed using the validation set. Results: Of 921 final participants (624 male; mean age ± standard deviation, 31.5 ± 9.0 years), the development and validation sets comprised 497 and 424 patients, respectively. In the development set, the areas under the ROC curve for diagnosing hepatic steatosis were 0.920 for CS-MRI-PDFF and 0.915 for HISTO-MRS-PDFF. For ruling-out hepatic steatosis, the CS-MRI-PDFF cutoff was 2.3% (sensitivity, 92.4%; specificity, 63.0%) and the HISTO-MRI-PDFF cutoff was 2.6% (sensitivity, 88.8%; specificity, 70.1%). For ruling-in hepatic steatosis, the CS-MRI-PDFF cutoff was 3.5% (sensitivity, 73.5%; specificity, 88.6%) and the HISTO-MRI-PDFF cutoff was 4.0% (sensitivity, 74.7%; specificity, 90.6%). Conclusion: In a large population of healthy adults, our study suggests diagnostic thresholds for ruling-out and ruling-in hepatic steatosis defined as HFF ≥ 5% by contemporary PDFF measurement methods.

• Korean Journal of Radiology
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• 제23권11호
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• pp.1078-1088
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• 2022

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. Results: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. Conclusion: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

• Korean Journal of Radiology
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• 제20권8호
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• pp.1293-1299
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• 2019

Objective: The purpose of this study was to evaluate the diagnostic performance of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for chronic empyema-associated malignancy (CEAM). Materials and Methods: We retrospectively reviewed the ¹⁸F-FDG PET/CT images of 33 patients with chronic empyema, and analyzed the following findings: 1) shape of the empyema cavity, 2) presence of fistula, 3) maximum standardized uptake value (SUV) of the empyema cavity, 4) uptake pattern of the empyema cavity, 5) presence of a protruding soft tissue mass within the empyema cavity, and 6) involvement of adjacent structures. Final diagnosis was determined based on histopathology or clinical follow-up for at least 6 months. The abovementioned findings were compared between the ¹⁸F-FDG PET/CT images of CEAM and chronic empyema. A receiver operating characteristic (ROC) analysis was also performed. Results: Six lesions were histopathologically proven as malignant; there were three cases of diffuse large B-cell lymphoma, two of squamous cell carcinoma, and one of poorly differentiated carcinoma. Maximum SUV within the empyema cavity (p < 0.001) presence of a protruding soft tissue mass (p = 0.002), and involvement of the adjacent structures (p < 0.001) were significantly different between the CEAM and chronic empyema images. The maximum SUV exhibited the highest diagnostic performance, with the highest specificity (96.3%, 26/27), positive predictive value (85.7%, 6/7), and accuracy (97.0%, 32/33) among all criteria. On ROC analysis, the area under the curve of maximum SUV was 0.994. Conclusion: ¹⁸F-FDG PET/CT can be useful for diagnosing CEAM in patients with chronic empyema. The maximum SUV within the empyema cavity is the most accurate ¹⁸F-FDG PET/CT diagnostic criterion for CEAM.