• 한국노년학
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• 제39권2호
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• pp.241-259
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• 2019

본 연구는 노인 자원봉사활동의 성과를 성공적 노화나 생산적 노화가 아닌 노인의 자기효능감과 자기확장성의 관점에서 연구하고자 하였다. 이를 통해 노년기에 일어날 수 있는 내적성장과 자가의 확장을 검증 할 수 있으며, 노년기 삶을 재조명하는 계기를 마련하는데 연구의 목적이 있다. 이러한 연구목적을 검증하기 위하여 부산광역시의 시청 및 구청에서 진행하는 자원봉사활동에 참여한 노인 300명을 연구대상으로 하여, 2018년 9월부터 11월까지 약 3개월에 걸쳐 설문지를 배부하여 불성실하게 응답되었다고 판단된 34부의 설문지를 제외한 후 266부의 자료를 본 연구에 사용하였다. 연구결과는 다음과 같다. 첫째, 노인의 자원봉사활동참여 동기(가치기능, 사회기능, 이해기능)는 자기효능감에 긍정적인 영향을 미치는 것으로 나타났다. 둘째, 자원봉사활동참여 동기(가치기능, 사회기능, 이해기능)는 자기확장성에 긍정적인 영향을 미치는 것으로 나타났다. 셋째, 노인의 자기효능감(자기조절 효능감, 자신감)은 자기확장성에 긍정적인 영향을 미치는 것으로 나타났다. 넷째, 자기효능감((자기조절 효능감, 자신감)은 노인의 자원봉사활동 참여동기와 자기확장성의 관계에서 매개효과가 발생하는 것으로 나타났다. 본 연구는 노인의 자원봉사활동 참여동기가 자기효능감과 자기확장성에 영향을 미친다는 관련성을 규명 하였다. 특히, 노인에게 심리사회적 개념인 자기효능감의 매개변수를 활용하여, 노인복지의연구의 새로운 관점을 제공함으로써 향후 노인의 자원봉사활동 활성화를 위한 실천적, 정책적 방안을 제시하였다는데 연구의 의의가 있다.

• Journal of Digestive Cancer Research
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• 제6권1호
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• pp.25-31
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• 2018

Background: In Korea, stomach cancer is the second most common malignancy and the third leading cause of cancer-related deaths. the time of diagnosis is very important for treatment so early detection and surgery are currently considered the mainstay of treatment, when diagnosed advanced with tumor extension through the gastric wall and direct extension into other organs, with metastatic involvement. Recently, new drugs, drug combinations, and multimodal approaches have been used to treat this disease and In cancers over expressing or amplifying HER2, the combination of cisplatin-fluoropyrimidine-trastuzumab is considered to be the treatment of reference. but At present, the choice of treatment schedule for HER2-negative tumors is based on the medical institution's preferences and adverse effects profile. The aim of this study was to evaluate the effectiveness and safety of using FOLFOX regimen as a first-line therapy or a salvage therapy in the patients with HER2-negative advanced or metastatic gastric cancer. Methods: We retrospective reviewed the patient medical record from March 2012 to July 2017. This study evaluated 113 patients. Sixty-eight patients were treated with the FOLFOX regimen for the first time (first-line group) and 45 patients were treated with the FOLFOX regimen as a second (35 patients) or third (10 patients) chemotherapy (salvage group). Results: In the first-line group, the response rate was 54.9%. In the salvage therapy group, the response rate was 24.4% and The difference was statistically significant (p=0.205). The median TTP of the first-line group was 10.7 months (95% confidence interval [95% CI], 7.8-13.7 months) and that of salvage line group was 6.1 months (95% CI, 3.8-8.4 months). The median OS of the first-line group was 15.8 months (95% CI, 12.7-18.9 months) and that of the salvage therapy group was 10.2 months (95% CI, 8.2-11.9 months). drug toxicity was similar andtolerable between two groups. Conclusion: In patients with unresctable metastatic gastric cancer, after failing to respond to first-line therapy, most patients have no alternative other than second-line therapy because the disease is highly progressive. if the performance status of the patient is good enough to be eligible to treatments beyond best supportive care. FOLFOX regimen can be a considerable therapeutic option for salvage treatment.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제26권4호
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• pp.333-348
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• 2023

인공지능 기반의 수학 디지털교과서의 가장 핵심적인 기능으로 여겨지는 개별 맞춤형 교수·학습이 실현되기 위해서는 개별 학생의 여러 가지 특성 요인에 대한 명확한 분석과 진단이 가장 관건이다. 본 연구에서는 수학 AI 디지털교과서에서 개별 맞춤형 학습 진단을 위한 분석 요인과 도구, 데이터 수집·분석을 위한 구축 모델을 도출하였다. 이를 위하여 최근 교육부의 AI 디지털교과서 적용 계획에 따른 수학 AI 디지털교과서에 대한 요구, 개별화 맞춤형 학습과 이를 위한 데이터에 대한 선행 연구, 수학 디지털플랫폼에서 학습자 분석에 대한 요인 등이 검토되었다. 연구 결과, 연구자는 학생 개인별로 수집해야 할 데이터로 학습 분석을 위한 요인으로 학습 준비도, 과정 및 수행도, 성취도, 취약점, 성향 분석을 위한 요인으로 학습 지속 시간, 문제해결에 걸린 시간, 집중도, 수학학습 습관, 정서 분석을 위한 요인으로 자신감, 흥미, 불안, 학습의욕, 가치 인식, 태도 분석을 위한 요인으로 자기 관리, 학습 전략으로 정리하였다. 또한, 이러한 요인에 대한 데이터 수집 도구로, 문제에 대한 정오 데이터, 학습 진도율, 학생 활동에 대한 화면 녹화 자료, 이벤트 데이터, 시선 추적 장치, 자기 응답 설문 등을 제안하였다. 최종적으로 이러한 요인들을 학습 전, 중, 후로 시계열화한 데이터 수집 모델이 제안되었다.

• 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.

• 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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• 제24권5호
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• pp.454-464
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• 2023

Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. Results: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. Conclusion: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

• Korean Journal of Radiology
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• 제24권5호
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• pp.395-405
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• 2023

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

• Korean Journal of Radiology
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• 제24권2호
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• pp.133-144
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• 2023

Objective: Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. Materials and Methods: Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively. Conclusion: The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.

• Korean Journal of Radiology
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• 제23권8호
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• Korean Journal of Radiology
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• 제23권10호
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• pp.986-997
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• 2022

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.