• 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.

• Nutrition Research and Practice
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• 제14권2호
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• pp.102-108
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• 2020

BACKGROUND/OBJECTIVES: In Oriental medicine, certain foods may be beneficial or detrimental based on an individual's constitution; however, the scientific basis for this theory is insufficient. The purpose of this study was to investigate the effect of body constitution, based on the Sasang type of Korean traditional medical classification system, on the bioavailability of soy isoflavones of Cheonggukjang, a quick-fermented soybean paste. SUBJECTS/METHODS: A pilot study was conducted on 48 healthy Korean men to evaluate the bioavailability of isoflavone after ingestion of food based on constitution types classified by the Sasang typology. The participants were classified into the Taeeumin (TE; n = 15), Soyangin (SY; n = 15), and Soeumin (SE; n = 18) groups. Each participant ingested 50 g of Cheonggukjang per 60 kg body weight. Thereafter, blood was collected, and the soy isoflavone metabolites were analyzed by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. Ntrikinetic analysis of individual isoflavone-derived metabolites was performed. RESULTS: Our nutrikinetic analysis identified 21 metabolites derived from isoflavones in the blood samples from 48 healthy Korean men (age range, 21-29 years). Significant differences were observed in the time to maximum concentration (T_max) and elimination half-life (t_1/2) for nine metabolites among the three groups. The T_max and t_1/2 of the nine metabolites were higher in the SE group than in the other groups. Moreover, the absorption rates, as determined by the area under the plasma-level curve (AUC) values of intact isoflavone, were 5.3 and 9.4 times higher in the TE group than in the SY and SE groups, respectively. Additionally, the highest AUC values for phase I and II metabolites were observed in the TE group. CONCLUSIONS: These findings indicate that isoflavone bioavailability, following Cheonggukjang insgestion, is high in individuals with the TE constitution, and relatively lower in those with the SE and SY constitutions.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5095-5099
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• 2015

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materials and Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (${\geq}65$ years), use of antibiotics, low serum albumin concentrations (${\leq}37.18g/L$), radiotherapy, surgery, low hemoglobin hyperlipidemia (${\leq}93.67g/L$), long time of hospitalization (${\geq}14$days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model($0.829{\pm}0.019$)was higher than that of LR model ($0.756{\pm}0.021$). Conclusions: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

• 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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• 제24권6호
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• pp.541-552
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• 2023

Objective: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. Materials and Methods: A model that can utilize "pneumoperitoneum" and "non-pneumoperitoneum" classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. Results: In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers' performances improved with the assistance of the proposed model. Conclusion: The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.

• Korean Journal of Radiology
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• 제23권10호
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• pp.939-948
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• 2022

Objective: Evidence supports the efficacy of coronary computed tomography angiography (CCTA)-based risk scores in cardiovascular risk stratification of patients with suspected coronary artery disease (CAD). We aimed to compare two CCTA-based risk score algorithms, Leiden and Confirm scores, in patients with diabetes mellitus (DM) and suspected CAD. Materials and Methods: This single-center prospective cohort study consecutively included 1241 DM patients (54.1% male, 60.2 ± 10.4 years) referred for CCTA for suspected CAD in 2015-2017. Leiden and Confirm scores were calculated and stratified as < 5 (reference), 5-20, and > 20 for Leiden and < 14.3 (reference), 14.3-19.5, and > 19.5 for Confirm. Major adverse cardiovascular events (MACE) were defined as the composite outcomes of cardiovascular death, nonfatal myocardial infarction (MI), stroke, and unstable angina requiring hospitalization. The Cox model and Kaplan-Meier method were used to evaluate the effect size of the risk scores on MACE. The area under the curve (AUC) at the median follow-up time was also compared between score algorithms. Results: During a median follow-up of 31 months (interquartile range, 27.6-37.3 months), 131 of MACE were recorded, including 17 cardiovascular deaths, 28 nonfatal MIs, 64 unstable anginas requiring hospitalization, and 22 strokes. An incremental incidence of MACE was observed in both Leiden and Confirm scores, with an increase in the scores (log-rank p < 0.001). In the multivariable analysis, compared with Leiden score < 5, the hazard ratios for Leiden scores of 5-20 and > 20 were 2.37 (95% confidence interval [CI]: 1.53-3.69; p < 0.001) and 4.39 (95% CI: 2.40-8.01; p < 0.001), respectively, while the Confirm score did not demonstrate a statistically significant association with the risk of MACE. The Leiden score showed a greater AUC of 0.840 compared to 0.777 for the Confirm score (p < 0.001). Conclusion: CCTA-based risk score algorithms could be used as reliable cardiovascular risk predictors in patients with DM and suspected CAD, among which the Leiden score outperformed the Confirm score in predicting MACE.

• Asian Pacific Journal of Cancer Prevention
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• 제16권10호
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• pp.4223-4228
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• 2015

Objective: To investigate the diagnostic value of the R-way colposcopic evaluation system (R-way system) in cervical cancer screening. Materials and Methods: Between August 2013 and August 2014, a total of 1,059 cases referred to colposcopy in Peking University First Hospital were studied using both the R-way system and conventional colposcopy. Our study evaluated and compared the diagnostic ability of the two methods in detecting high-grade lesions and cervical cancer (hereinafter called CIN2+). Evaluation indicators including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Youden index and the area under the curve (AUC) of the receiver operating characteristic (ROC) were calculated. Results: The R-way system had a slightly lower specificity (94.5%) than conventional colposcopy (96.0%) for CIN2+ detection (P=0.181). However, the sensitivity (77.8%) was significantly higher than with the conventional colposcopic method (46.6%) (${\chi}^2=64.351$, P<0.001). In addition, the AUC of the ROC for CIN2+ detection using the R-way system (0.839) was larger than that with conventional colposcopy (0.731) (Z=4.348, P<0.001). If preliminary result had been drawn from cervical exfoliated cytology before colposcopy referral, combination of the R-way system with cytology could increase the sensitivity to 93.9% for CIN2+ detection (excluding ASCUS\LSIL), confirmed by multipoint biopsy or ECC. Conclusions: The diagnostic value of the R-way evaluation system is higher than that of conventional colposcopic evaluation in cervical cancer screening. Moreover, taking the ease of use and standardized quality control management into account, the R-way system is highly preferable.

• 정보보호학회논문지
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• 제30권4호
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• pp.617-629
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• 2020

인터넷 컴퓨팅 환경의 변화, 새로운 서비스 출현, 그리고 지능화되어 가는 해커들의 다양한 공격으로 인한 규칙 기반 침입탐지시스템의 한계점을 극복하기 위해 기계학습 및 딥러닝 기술을 활용한 네트워크 이상 검출(NAD: Network Anomaly Detection)에 대한 관심이 집중되고 있다. NAD를 위한 대부분의 기존 기계학습 및 딥러닝 기술은 '정상'과 '공격'으로 레이블링된 훈련용 데이터 셋을 학습하는 지도학습 방법을 사용한다. 본 논문에서는 공격의 징후가 없는 일상의 네트워크에서 수집할 수 있는 레이블링이 필요 없는 데이터 셋을 이용하는 비지도학습 오토 엔코더(AE: AutoEncoder)를 활용한 NAD 적용 가능성을 제시한다. AE 성능을 검증하기 위해 NSL-KDD 훈련 및 시험 데이터 셋을 사용해 정확도, 정밀도, 재현율, f1-점수, 그리고 ROC AUC (Receiver Operating Characteristic Area Under Curve) 값을 보인다. 특히 이들 성능지표를 대상으로 AE의 층수, 규제 강도, 그리고 디노이징 효과 등을 분석하여 레퍼런스 모델을 제시하였다. AE의 훈련 데이터 셋에 대한 재생오류 82-th 백분위수를 기준 값으로 KDDTest+와 KDDTest-21 시험 데이터 셋에 대해 90.4%와 89% f1-점수를 각각 보였다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권21호
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• pp.9301-9305
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• 2014

Background: Pain is one of the most terrifying symptoms for cancer patients. Although most patients with cancer pain need opioids, complete relief of pain is hard to achieve. This study investigated the factors influencing persistent pain-free survival (PPFS) and opioid efficiency. Materials and Methods: A prospective study was conducted on 100 patients with cancer pain, hospitalized at the medical oncology clinic of Akdeniz University. Patient records were collected including patient demographics, the disease, treatment characteristics, and details of opioid usage. Pain intensity was measured using a patient self-reported visual analogue scale (VAS). The area under the curve (AUC) reflecting the pain load was calculated from daily VAS tables. PPFS, the primary measure of opioid efficacy, was described as the duration for which a patient reported a greater than or equal to two-point decline in their VAS for pain. Predictors of opioid efficacy were analysed using a multivariate analysis. Results: In the multivariate analysis, PPFS was associated with the AUC for pain (Exp (B)=0.39 (0.23-0.67), P=0.001), the cumulative opioid dosage used during hospitalisation (Exp (B)=1.00(0.99-1.00), P=0.003) and changes in the opioid dosage (Exp (B)=1.01 (1.00-1.01), P=0.016). The change in VAS score over the standard dosage of opioids was strongly associated with current cancer treatment (chemotherapy vs. others) (${\beta}=-0.31$, T=-2.81, P=0.007) and the VAS for pain at the time of hospitalisation (${\beta}=-0.34$, T=-3.07, P= 0.003). Conclusions: The pain load, opioid dosage, concurrent usage of chemotherapy and initial pain intensity correlate with the benefit received from opioids in cancer patients.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1142-1150
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• 2023

Objective: To evaluate ^99mtechnetium-three polyethylene glycol spacers-arginine-glycine-aspartic acid (^99mTc-3PRGD₂) single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging for diagnosing lymph node metastasis of primary malignant lung neoplasms. Materials and Methods: We prospectively enrolled 26 patients with primary malignant lung tumors who underwent ^99mTc-3PRGD₂ SPECT/CT and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT imaging. Both imaging methods were analyzed in qualitative (visual dichotomous and 5-point grades for lymph nodes and lung tumors, respectively) and semiquantitative (maximum tissue-to-background radioactive count) manners for the lymph nodes and lung tumors. The performance of the differentiation of lymph nodes with and without metastasis was determined at the per-lymph node station and per-patient levels using histopathological results as the reference standard. Results: Total 42 stations had metastatic lymph nodes and 136 stations had benign lymph nodes. The differences between metastatic and benign lymph nodes in the visual qualitative and semiquantitative analyses of ^99mTc-3PRGD₂ SPECT/CT and ¹⁸F-FDG PET/CT were statistically significant (all P < 0.001). The area under the receiver operating characteristic curve (AUC) in the semi-quantitative analysis of ^99mTc-3PRGD₂ SPECT/CT was 0.908 (95% confidence interval [CI], 0.851-0.966), and the sensitivity, specificity, positive predictive value, and negative predictive value were 0.86 (36/42), 0.88 (120/136), 0.69 (36/52), and 0.95 (120/126), respectively. Among the 26 patients (including two patients each with two lung tumors), 15 had pathologically confirmed lymph node metastasis. The difference between primary lung lesions in patients with and without lymph node metastasis was statistically significant only in the semi-quantitative analysis of ^99mTc-3PRGD₂ SPECT/CT (P = 0.007), with an AUC of 0.807 (95% CI, 0.641-0.974). Conclusion: ^99mTc-3PRGD₂ SPECT/CT imaging may notably perform in the direct diagnosis of lymph node metastasis of primary malignant lung tumors and indirectly predict the presence of lymph node metastasis through uptake in the primary lesions.