• Asian Pacific Journal of Cancer Prevention
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• v.15 no.17
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• pp.7433-7436
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• 2014

Background: Gastric cancer (GC) is the second cause of cancer related death in the world. It may develop by progression from its precancerous condition, called gastric atrophy (GA) due to gastritis. The aim of this study was to evaluate the accuracy of serum levels of pepsinogens (Pg) and gastrin-17 (G17) as non-invasive methods to discriminate GA or GC (GA/GC) patients. Materials and Methods: Subjects referred to gastrointestinal clinics of Golestan province of Iran during 2010 and 2011 were invited to participate. Serum levels of PgI, PgII and G17 were measured using a GastroPanel kit. Based on the pathological examination of endoscopic biopsy samples, subjects were classified into four groups: normal, non-atrophic gastritis, GA, and GC. Receiver operating curve (ROC) analysis was used to determine cut-off values. Indices of validity were calculated for serum markers. Results: Study groups were normal individuals (n=74), non-atrophic gastritis (n=90), GA (n=31) and GC patients (n=30). The best cut-off points for PgI, PgI/II ratio, G17 and HP were $80{\mu}g/L$, 10, 6 pmol/L, and 20 EIU, respectively. PgI could differentiate GA/GC with high accuracy (AUC=0.83; 95%CI: 0.76-0.89). The accuracy of a combination of PgI and PgI/II ratio for detecting GA/GC was also relatively high (AUC=0.78; 95%CI: 0.70-0.86). Conclusions: Our findings suggested PgI alone as well as a combination of PgI and PgI/II ratio are valid markers to differentiate GA/GC. Therefore, Pgs may be considered in conducting GC screening programs in high-risk areas.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5043-5047
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• 2015

Background: Relatively little is known with certainty about the status and role of p53 or MDM2 in predicting prognosis and survival of renal cell carcinoma. The present study aimed to determine the value of P53 and MDM2 over-expression, alone and simultaneously, to predict five-year survival of patients with kidney cancer in Iran. Materials and Methods: Patients with kidney cancer referred to Hasheminejad Kidney Center between 2007 and 2009, underwent radical nephrectomy and had pathology reports of clear cell, papillary or chromophobe renal cell carcinoma were included in our cohort study. Other histological types of renal cell carcinoma were not included. The patients with missed, incomplete or poor quality paraffin blocks were also excluded. Overall ninety one patients met the inclusion and exclusion criteria. To assess the histopathological features of the tumor, immunohistochemical (IHC) staining of formalin fixed, paraffin-embedded tumor samples were performed. The five-year survival was determined by the patients' medical files and telephone following-up. Results: In total, 1.1% of all samples were revealed to be positive for P53. Also, 20.8% of all samples were revealed to be positive for MDM2.The patients were all followed for 5 years. In this regard, 5-year mortality was 30.5% and thus 5-year survival was 85.3%. According to the Cox proportional hazard analysis, positive P53 marker was only predictor for patients' 5-year survival that the presence of positive p53 increased the risk for long-term mortality up to 2.8 times (HR=2.798, 95%CI: 1.176-6.660, P=0.020). However, the presence of MDM2 could not predict long-term mortality. In this regard, analysis by the ROC curve showed a limited role for predicting long-term survival by confirming P53 positivity (AUC=0.610, 95%CI: 0.471-.750, P=0.106). The best cutoff point for P53 to predict mortality was 0.5 yielding a low sensitivity (32.0%) but a high specificity (97.9%). In similar analysis, measurement of MDM2 positivity could not predict mortality (AUC=0.449, 95%CI: 0.316-.583, P=0.455). Conclusions: The simultaneous presence of both P53 and MDM2 markers in our population is a rare phenomenon and the presence of these markers may not predict long-term survival in patients who undergoing radical nephrectomy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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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.

• Journal of Environmental Impact Assessment
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• v.22 no.3
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• pp.219-230
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• 2013

Species distribution modeling is one of the most effective habitat analysis methods for wildlife conservation. This study was for evaluating the suitability of species distribution to distance between forest patches in Seoul city using tits. We analyzed the distribution of the four species of tits: varied tit (Parus varius), marsh tit (P. palustris), great tit (P. major) and coal tit (P. ater), using the landscape indexes and connectivity indexes, and compared the resulting suitability indexes from 100m to 1,000m. As factors affecting to the distribution of tits, we calculated landscape indices by separating them into intra-patch indices (i.e. logged patch area (PA), area-weighted mean patch shape index (PSI), tree rate (TR)) and inter-patch indices (i.e. patch degree (PD), patch betweenness (PB), difference probability of connectivity (DPC)), to analyze the internal properties of the patches and their connectivity by tits occurrence data using logistic regression modeling. The models were evaluated by AICc (Akaike Information Criteria with a correction for finite sample sizes) and AUC (Area Under Curve of ROC). The results of AICc and AUC showed DPC, PA, PSI, and TR were important factors of the habitat models for great tit and marsh tit at the level of distance 500~800m. In contrast, habitat models for coal tit and varied tit, which are known as forest interior species, reflected PA, PSI, and TR as intra-patch indices rather than connectivity. These mean that coal tit and varied tit are more likely to find a large circular forest patch than a small and long-shaped forest patch, which are higher rate of forest. Therefore, different strategies are required in order to enhance the habitats of the forest birds, tits, in a region that has fragmented forest patches such as Seoul city. It is important to manage forest interior areas for coal tit and varied tit, which are known as forest interior species and to manage not only forest interior areas but also connectivity of the forest patches in the threshold distance for great tit and marsh tit as adapted species to the urban ecosystem for sustainable ecosystem management.

• Journal of Dental Anesthesia and Pain Medicine
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• v.20 no.5
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• pp.293-301
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• 2020

Background: The success rate of intubation under direct laryngoscopy is greatly influenced by laryngoscopic grade using the Cormack-Lehane classification. However, it is not known whether grade under direct laryngoscopy can also affects the success rate of nasotracheal intubation using a fiberoptic bronchoscpe, so this study investigated the same. In addition, we investigated other factors that influence the success rate of fiberoptic nasotracheal intubation (FNI). Methods: FNI was performed by 18 anesthesiology residents under general anesthesia in patients over 15 years of age who underwent elective oral and maxillofacial operations. In all patients, the Mallampati grade was measured. Laryngeal view grade under direct laryngoscopy, and the degree of secretion and bleeding in the oral cavity was measured and divided into 3 grades. The time required for successful FNI was measured. If the intubation time was > 5 minutes, it was evaluated as a failure and the airway was managed by another method. The failure rate was evaluated using appropriate statistical method. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were also measured. Results: A total of 650 patients were included in the study, and the failure rate of FNI was 4.5%. The patient's sex, age, height, weight, Mallampati, and laryngoscopic view grade did not affect the success rate of FNI (P > 0.05). BMI, the number of FNI performed by residents (P = 0.03), secretion (P < 0.001), and bleeding (P < 0.001) grades influenced the success rate. The AUCs of bleeding and secretion were 0.864 and 0.798, respectively, but the AUC of BMI, the number of FNI performed by residents, Mallampati, and laryngoscopic view grade were 0.527, 0.616, 0.614, and 0.544, respectively. Conclusion: Unlike in intubation under direct laryngoscopy, in the case of FNI, oral secretion and nasal bleeding had a significant effect on FNI difficulty than Mallampati grade or Laryngeal view grade.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.217-228
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• 2021

This paper discusses a methodology where an integrated modelling framework is used to quantify the risk derived from anthropic activities on habitats and species. To achieve this purpose, a tool comprising the Delft3D and HABITAT model, was applied in the Yeongsan river. Delft3D effectively simulated the operational condition and flow of weirs in river. In accuracy evaluation of the Delft3D-FLOW, the Bias, Pbias, Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Index of Agreement (IOA) were used, and the result was evaluated as grade above 'Satisfactory'. The HABITAT calculated Habitat Suitability Value (HSV) for the following eight species: mammal, fish, aquatic plant, and benthic macroinvertebrate. An Area was defined as a suitable habitat if the HSV was larger than 0.5. HABITAT was judged accurately by measuring the Correct Classification rate (CCR) and the area under the ROC curve (AUC). For benthic macroinvertebrate, the CCR and AUC were 77% and 0.834, respectively, at thresholds of 0.017 and 4 inds/m² for HSV and individuals per unit area. This meant that the HABITAT model accurately predicted the appearance of the benthic macroinvertebrates by approximately 77% and that the probability of false alarms was also very low. As a result of evaluating the suitability of habitats, in the Yeongsan river, if the annual "lowest level" (Seungchon weir: 2.5 EL.m/ Juksan weir: -1.35 EL.m) was maintained, the average habitat improvement effect of 6.5%P compared to the 'reference' scenario was predicted. Consequently, it was demonstrated that the integrated modelling framework for habitat suitability assessment is able to support the remedy aquatic ecological management.

• The Korean Journal of Pain
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• v.34 no.2
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• pp.229-233
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• 2021

Background: Iliotibial band friction syndrome (ITBFS) is a common disorder of the lateral knee. Previous research has reported that the iliotibial band (ITB) thickness (ITBT) is correlated with ITBFS, and ITBT has been considered to be a key morphologic parameter of ITBFS. However, the thickness is different from inflammatory hypertrophy. Thus, we made the ITB cross-sectional area (ITBCSA) a new morphological parameter to assess ITBFS. Methods: Forty-three patients with ITBFS group and from 43 normal group who underwent T1W magnetic resonance imaging were enrolled. The ITBCSA was measured as the cross-sectional area of the ITB that was most hypertrophied in the magnetic resonance axial images. The ITBT was measured as the thickest site of ITB. Results: The mean ITBCSA was 25.24 ± 6.59 ㎟ in the normal group and 38.75 ± 9.11 ㎟ in the ITBFS group. The mean ITBT was 1.94 ± 0.41 mm in the normal group and 2.62 ± 0.46 mm in the ITBFS group. Patients in ITBFS group had significantly higher ITBCSA (P < 0.001) and ITBT (P < 0.001) than the normal group. A receiver operator characteristic curve analysis demonstrated that the best cut-off value of the ITBT was 2.29 mm, with 76.7% sensitivity, 79.1% specificity, and area under the curve (AUC) 0.88. The optimal cut-off score of the ITBCSA was 30.66 ㎟, with 79.1% sensitivity, 79.1% specificity, and AUC 0.87. Conclusions: ITBCSA is a new and sensitive morphological parameter for diagnosing ITBFS, and may even be more accurate than ITBT.

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.104-116
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• 2022

The purpose of this study is to systematically determine an optimal percentile cut-off in histogram analysis for calculating the mean parameters obtained from a non-Gaussian continuous-time random-walk (CTRW) diffusion model for differentiating individual glioma grades. This retrospective study included 90 patients with histopathologically proven gliomas (42 grade II, 19 grade III, and 29 grade IV). We performed diffusion-weighted imaging using 17 b-values (0-4000 s/mm²) at 3T, and analyzed the images with the CTRW model to produce an anomalous diffusion coefficient (D_m) along with temporal (𝛼) and spatial (𝛽) diffusion heterogeneity parameters. Given the tumor ROIs, we created a histogram of each parameter; computed the P-values (using a Student's t-test) for the statistical differences in the mean D_m, 𝛼, or 𝛽 for differentiating grade II vs. grade III gliomas and grade III vs. grade IV gliomas at different percentiles (1% to 100%); and selected the highest percentile with P < 0.05 as the optimal percentile. We used the mean parameter values calculated from the optimal percentile cut-offs to do a receiver operating characteristic (ROC) analysis based on individual parameters or their combinations. We compared the results with those obtained by averaging data over the entire region of interest (i.e., 100th percentile). We found the optimal percentiles for D_m, 𝛼, and 𝛽 to be 68%, 75%, and 100% for differentiating grade II vs. III and 58%, 19%, and 100% for differentiating grade III vs. IV gliomas, respectively. The optimal percentile cut-offs outperformed the entire-ROI-based analysis in sensitivity (0.761 vs. 0.690), specificity (0.578 vs. 0.526), accuracy (0.704 vs. 0.639), and AUC (0.671 vs. 0.599) for grade II vs. III differentiations and in sensitivity (0.789 vs. 0.578) and AUC (0.637 vs. 0.620) for grade III vs. IV differentiations, respectively. Percentile-based histogram analysis, coupled with the multi-parametric approach enabled by the CTRW diffusion model using high b-values, can improve glioma grading.

• Journal of The Geomorphological Association of Korea
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• v.27 no.1
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• pp.61-89
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• 2020

The objective of this study is to characterize landslide susceptibility depending on various geo-environmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of 'very high' and 'high', was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.