• 한국환경복원기술학회지
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• 제26권5호
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• pp.19-32
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• 2023

The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.

• 한국시뮬레이션학회논문지
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• 제32권4호
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• pp.19-26
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• 2023

전국 대학생의 중도 탈락 비율의 증가는 학생 개인 뿐만 아니라 대학과 사회에 심각한 부정적 영향을 끼친다. 본 연구에서는 중도 탈락이 예상되는 학생을 사전에 식별하기 위하여, 각 대학의 학사관리 시스템에서 손쉽게 얻을 수 있는 학적 데이터를 기반으로 머신러닝 분야의 결정트리, 랜덤 포레스트, 로지스틱 회귀 및 딥러닝 기반의 중도 탈락 예측 모델을 구축하고, 그 성능을 비교·분석하였다. 분석 결과 로지스틱 회귀 기반 예측 모델의 재현율이 가장 높았으나 f-1 및 auc 값이 낮은 한계를 보였고, 랜덤 포레스트 기반의 예측 모델의 경우 재현율을 제외한 다른 모든 지표에서 가장 우수한 성능을 보였다. 또한 예측 기간에 따른 예측 모델의 성능을 확인하기 위하여 예측 기간을 단기(1개 학기 이내), 중기(2개 학기 이내) 및 장기(3개 학기 이내)로 나누어 분석해 본 결과, 장기 예측 시 가장 높은 예측력을 보였다. 본 연구를 통해 각 대학은 중도 탈락이 예상되는 학생들을 조기에 식별하고, 이들에 대한 집중 관리를 통해 중도 탈락 비율을 줄이며 나아가 대학 재정 안정화에 기여할 수 있을 것으로 기대된다.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제34권4호
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• pp.258-267
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• 2023

Objectives: This study aimed to compare the utility of the Psychoeducational Profile-Revised (PEP-R), Korean Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (K-WPPSI-IV), and Vineland Adaptive Behavior Scale, Second Edition (VABS-II) for evaluating developmental disabilities (DD) in preschool children. Additionally, we examined the correlations between the PEP-R, K-WPPSI-IV, and VABS-II. Methods: A total of 164 children aged 37-84 months were assessed. Children's development was evaluated using the PEP-R, K-WPPSI-IV, VABS-II, Preschool Receptive-Expressive Language Scale, and Korean Childhood Autism Rating Scale, Second Edition. Results: Of the 164 children, 103 had typical development (TD) and 61 had DD. The mean of the PEP-R Developmental Quotient (DQ), K-WPPSI-IV Full-Scale Intelligence Quotient (FSIQ), and VABS-II Adaptive Behavior Composite (ABC) scores were significantly higher in the TD group than in the DD group (p<0.001). The estimated area under the curve of the PEP-R DQ, K-WPPSI-IV FSIQ, and VABS-II ABC scores was 0.953 (95% confidence interval [CI]=0.915-0.992), 0.955 (95% CI=0.914-0.996), and 0.961 (95% CI=0.932-0.991), respectively, which did not indicate a statistically significant difference. The PEP-R DQ scores were positively correlated with the K-WPPSI-IV FSIQ (r=0.90, p<0.001) and VABS-II ABC scores (r=0.84, p<0.001). A strong correlation was observed between the K-WPPSI-IV FSIQ and VABS-II ABC scores (r=0.89, p<0.001). Conclusion: This study found that the PEP-R, K-WPPSI-IV, and VABS-II effectively distinguished DD from TD in preschool children, and no significant differences in utility were observed between them.

• Journal of Yeungnam Medical Science
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• 제40권4호
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• pp.388-393
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• 2023

Background: Differentiating between bacterial and nonbacterial colitis remains a challenge. We aimed to evaluate the value of serum procalcitonin (PCT) and C-reactive protein (CRP) in differentiating between bacterial and nonbacterial colitis. Methods: Adult patients with three or more episodes of watery diarrhea and colitis symptoms within 14 days of a hospital visit were eligible for this study. The patients' stool pathogen polymerase chain reaction (PCR) testing results, serum PCT levels, and serum CRP levels were analyzed retrospectively. Patients were divided into bacterial and nonbacterial colitis groups according to their PCR. The laboratory data were compared between the two groups. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: In total, 636 patients were included; 186 in the bacterial colitis group and 450 in the nonbacterial colitis group. In the bacterial colitis group, Clostridium perfringens was the commonest pathogen (n=70), followed by Clostridium difficile toxin B (n=60). The AUC for PCT and CRP was 0.557 and 0.567, respectively, indicating poor discrimination. The sensitivity and specificity for diagnosing bacterial colitis were 54.8% and 52.6% for PCT, and 52.2% and 54.2% for CRP, respectively. Combining PCT and CRP measurements did not increase the discrimination performance (AUC, 0.522; 95% confidence interval, 0.474-0.571). Conclusion: Neither PCT nor CRP helped discriminate bacterial colitis from nonbacterial colitis.

• 한국산업보건학회지
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• 제34권1호
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• pp.57-66
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• 2024

Objective: Chronic obstructive pulmonary disease (COPD) is characterized by progressive airflow obstruction that is only partly reversible, inflammation in the airways, and systemic effects. This study aimed to investigate the association between low peripheral oxygen saturation levels (SpO₂), and composite indices predicting death in male patients with (COPD). Method: A total of 140 participants with post-bronchodilator FEV₁/FVC ratio less than 0.7 were included. Three composite indices (ADO, DOSE, BODEx) were calculated using six variables such as age (A), airflow obstruction (O), body mass index (B), dyspnea (D), exacerbation history (E or Ex), and smoking status (S). Severity of airflow limitation was classified according to Global Initiative for Obstructive Lung Disease (GOLD) guidelines. SpO₂ was measured by pulse oximetry, and anemia and iron deficiency were assessed based on blood hemoglobin levels and serum markers such as ferritin, transferrin saturation, or soluble transferrin receptor. Results: Participants with low SpO₂ (<95%) showed significantly lower levels of %FEV₁ predicted (p=0.020) and %FEV₁/FVC ratio (p=0.002) compared to those with normal SpO₂ levels. The mMRC dyspnea scale (p<0.001) and GOLD grade (p=0.002) showed a significant increase in the low SpO₂ group. Receiver Operating Characteristic analysis revealed higher area under the curve for %FEV₁ (p=0.020), %FEV₁/FVC(p=0.002), mMRC dyspnea scale (p=0.001), GOLD grade (p=0.010), ADO (p=0.004), DOSE (p=0.002), and BODEx (p=0.011) in the low SpO₂ group. Conclusion: These results suggest that low SpO₂ levels are related to increased airflow limitation and the composite indices of COPD.

• Korean Journal of Radiology
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• 제21권7호
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• pp.859-868
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• 2020

Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. Conclusion: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

• Korean Journal of Radiology
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• 제21권12호
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• pp.1317-1325
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• 2020

Objective: The aim of this study was to prospectively evaluate whether liver stiffness (LS) assessments, obtained by two-dimensional (2D)-shear wave elastography (SWE) with a propagation map, can evaluate liver fibrosis stage using histopathology as the reference standard. Materials and Methods: We prospectively enrolled 123 patients who had undergone percutaneous liver biopsy from two tertiary referral hospitals. All patients underwent 2D-SWE examination prior to biopsy, and LS values (kilopascal [kPa]) were obtained. On histopathologic examination, fibrosis stage (F0-F4) and necroinflammatory activity grade (A0-A4) were assessed. Multivariate linear regression analysis was performed to determine the significant factors affecting the LS value. The diagnostic performance of the LS value for staging fibrosis was assessed using receiver operating characteristic (ROC) analysis, and the optimal cut-off value was determined by the Youden index. Results: Reliable measurements of LS values were obtained in 114 patients (92.7%, 114/123). LS values obtained from 2D-SWE with the propagation map positively correlated with the progression of liver fibrosis reported from histopathology (p < 0.001). According to the multivariate linear regression analysis, fibrosis stage was the only factor significantly associated with LS (p < 0.001). The area under the ROC curve of LS from 2D-SWE with the propagation map was 0.773, 0.865, 0.946, and 0.950 for detecting F ≥ 1, F ≥ 2, F ≥ 3, and F = 4, respectively. The optimal cut-off LS values were 5.4, 7.8, 9.4, and 12.2 kPa for F ≥ 1, F ≥ 2, F ≥ 3, and F = 4, respectively. The corresponding sensitivity and specificity of the LS value for detecting cirrhosis were 90.9% and 88.4%, respectively. Conclusion: The LS value obtained from 2D-SWE with a propagation map provides excellent diagnostic performance in evaluating liver fibrosis stage, determined by histopathology.

• Korean Journal of Radiology
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• 제25권3호
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• pp.243-256
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• 2024

Objective: We aimed to investigate whether 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[¹⁸F]FDG PET/CT) can aid in evaluating the risk of malignancy in ampullary tumors detected by endoscopy. Materials and Methods: This single-center retrospective cohort study analyzed 155 patients (79 male, 76 female; mean age, 65.7 ± 12.7 years) receiving 2-[¹⁸F]FDG PET/CT for endoscopy-detected ampullary tumors 5-87 days (median, 7 days) after the diagnostic endoscopy between June 2007 and December 2020. The final diagnosis was made based on histopathological findings. The PET imaging parameters were compared with clinical data and endoscopic features. A model to predict the risk of malignancy, based on PET, endoscopy, and clinical findings, was generated and validated using multivariable logistic regression analysis and an additional bootstrapping method. The final model was compared with standard endoscopy for the diagnosis of ampullary cancer using the DeLong test. Results: The mean tumor size was 17.1 ± 7.7 mm. Sixty-four (41.3%) tumors were benign, and 91 (58.7%) were malignant. Univariable analysis found that ampullary neoplasms with a blood-pool corrected peak standardized uptake value in earlyphase scan (SUVe) ≥ 1.7 were more likely to be malignant (odds ratio [OR], 16.06; 95% confidence interval [CI], 7.13-36.18; P < 0.001). Multivariable analysis identified the presence of jaundice (adjusted OR [aOR], 4.89; 95% CI, 1.80-13.33; P = 0.002), malignant traits in endoscopy (aOR, 6.80; 95% CI, 2.41-19.20; P < 0.001), SUVe ≥ 1.7 in PET (aOR, 5.43; 95% CI, 2.00-14.72; P < 0.001), and PET-detected nodal disease (aOR, 5.03; 95% CI, 1.16-21.86; P = 0.041) as independent predictors of malignancy. The model combining these four factors predicted ampullary cancers better than endoscopic diagnosis alone (area under the curve [AUC] and 95% CI: 0.925 [0.874-0.956] vs. 0.815 [0.732-0.873], P < 0.001). The model demonstrated an AUC of 0.921 (95% CI, 0.816-0.967) in candidates for endoscopic papillectomy. Conclusion: Adding 2-[¹⁸F]FDG PET/CT to endoscopy can improve the diagnosis of ampullary cancer and may help refine therapeutic decision-making, particularly when contemplating endoscopic papillectomy.

• Korean Journal of Radiology
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• 제24권1호
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• pp.51-61
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• 2023

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent ¹⁸F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from ¹⁸F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

• Korean Journal of Radiology
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• 제24권6호
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.