• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5125-5135
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• 2015

Background: Total fat intake may be associated with increased risk of breast cancer, and fish oil has been suggested as a protection factor to breast cancer. But the effect of vegetable oils is inconclusive. We aimed to investigate the association with high vegetable oils consumption and breast cancer risk, and evaluated their dose-response relationship. Design: We systematically searched the MEDLINE, EMBASE, Cochrane databases, and CNKI updated to December 2014, and identified all observational studies providing quantitative estimates between breast cancer risk and different vegetable oils consumption. Fixed or random effect models were used to estimate summary odds ratios for the highest vs. lowest intake, and dose-response relationship was assessed by restricted cubic spline model and generalized least-squares trend (GLST) model. Results: Five prospective cohort studies and 11 retrospective case-control studies, involving 11,161 breast cancer events from more than 150,000 females, met the inclusion criteria. Compared with the lowest vegetable oils consumption, higher intake didn't increased the risk of breast cancer with pooled OR of 0.88 (95% CIs:0.77-1.01), and the result from dose-response analyses didn't show a significant positive or negative trend on the breast cancer risk for each 10g vegetable oil/day increment (OR=0.98, 95% CIs: 0.95-1.01). In the subgroup analyses, the oils might impact on females with different strata of BMI. Higher olive oil intake showed a protective effect against breast cancer with OR of 0.74 (95% CIs: 0.60-0.92), which was not significant among the three cohort studies. Conclusions: This meta-analyses suggested that higher intake of vegetable oils is not associated with the higher risk of breast cancer. Olive oil might be a protective factor for the cancer occurrence among case-control studies and from the whole. Recall bias and imbalance in study location and vegetable oils subtypes shouldn't be ignored. More prospective cohort studies are required to confirm the interaction of the impact of vegetable oils on different population and various cancer characteristic, and further investigate the relationship between different subtype oils and breast cancer.

• 재활치료과학
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• 제12권1호
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• pp.51-63
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• 2023

목적 : 본 연구에서는 빅데이터를 통해 정신과 질환을 가진 노인의 생활 습관 행태와 의료비 지출 크기 간의 연관성을 분석하여 생활 습관의 긍정적 영향 및 중요성을 제시하고 이에 관련한 중재의 근거를 제언하고자 하였다. 연구방법 : 건강보험공단에서 제공하는 노인 코호트 데이터베이스(Database; DB)의 2014, 2015년 자료를 활용하여 이차데이터 분석을 시행하였다. 연구 목적에 따라 전체 변수 중 생활 습관 행태와 연간 의료비 지출 크기를 추출하였다. 각각의 생활 습관 행태와 연간 의료비 지출 크기 사이의 연관성은 일반선형모형(Generalized linear model)을 사용하여 분석하였다. 결과 : 선정기준에 따라 추출된 총 32,853명의 데이터가 분석에 사용되었다. 12,617명(38.40%)의 남성과 20,236명(61.60%)의 여성으로 구성되어 있었다. 생활 습관과 의료비의 연관성을 분석한 결과, 비흡연자의 경우 흡연자보다 의료비 지출이 유의하게 낮았으며(estimate = -218,255원, p = .037), 일주일 중 30분 이상 걷는 일수가 증가할수록 의료비 지출이 유의하게 감소하였다(estimate = -58,843원, p < .0001). 반면, 일주일 중 술을 마시는 일수가 감소할수록 의료비 지출이 증가하는 양상이 나타나는 결과가 도출되었다(estimate = 692,289원, p < .0001). 결론 : 본 연구는 정신과 질환을 가지고 있는 노인에게 있어 생활 습관의 행태에 따른 의료비의 변화를 분석하였다. 흡연과 운동은 의료비 지출과 음의 연관성을 나타냈으며 걷기 운동을 많이 할수록, 흡연을 하지 않을수록 의료비 지출이 감소함을 알 수 있었다. 이러한 결과는 정신과 질환을 가지고 있는 노인에게 있어 건강한 생활 습관의 중요성을 시사한다. 본 연구가 정신과 질환을 가진 노인의 신체적, 정신적 건강 증진을 위한 생활 습관 관리 프로그램의 임상적 근거로써 활용될 수 있기를 기대한다.

• Korean Journal of Radiology
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• 제23권4호
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• pp.466-478
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• 2022

Objective: ¹⁸F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of ¹⁸F-FDG PET/CT in identifying malignancy in patients with HLH by combining ¹⁸F-FDG PET/CT and clinical parameters. Materials and Methods: Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment ¹⁸F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH). Results: The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of ¹⁸F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity. Conclusion: ¹⁸F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using ¹⁸F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of ¹⁸F-FDG PET/CT images.

• 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.

• 한국시스템다이내믹스연구
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• 제7권2호
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• pp.97-120
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• 2006

This paper deals with the extension of and discussion on the System Dynamics model (Jeong & Jeon, 2005) of river crabs in Korea. The previous model has been elaborated to empirically search for the optimal restoration and harvest rates of crabs in the Imjin River, on the basis of theoretical models of population dynamics in the field of bio-mathematics and environmental economics. In this paper, the authors tries to couple a series of new feedback loops related to density restrictions and cannibalistic behaviors with a stage-structured model of the crab ecosystem, and also to endogenize the parameter of baby crabs' survival that is caused by water quality improvement and income increase. Through these extensions and relaxations, the authors are able to argue about the strategic decision of the optimal rates additional considerations as well as the properties of the integrated system that was not covered in the previous paper.

• Environmental Analysis Health and Toxicology
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• 제29권
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• pp.12.1-12.8
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• 2014

Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to $10{\mu}m$ in diameter ($PM_{10}$) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly $PM_{10}$ data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average $PM_{10}$ concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared ($R^2$) statistics were computed. Results Mean annual average $PM_{10}$ concentrations in the seven major cities ranged between 45.5 and $66.0{\mu}g/m^3$ (standard deviation=2.40 and $9.51{\mu}g/m^3$, respectively). Cross-validated $R^2$ values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had $R^2$ values of zero. The national model produced a higher cross-validated $R^2$ (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate $PM_{10}$ source characteristics.

• Korean Journal of Radiology
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• 제22권3호
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.455-466
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• 2013

In nested case-control studies, the most common way to make inference under a proportional hazards model is the conditional logistic approach of Thomas (1977). Inclusion probability methods are more efficient than the conditional logistic approach of Thomas; however, the epidemiology research community has not accepted the methods as a replacement of the Thomas' method. This paper promotes the inverse probability weighting method originally proposed by Samuelsen (1997) in combination with an approximate jackknife standard error that can be easily computed using existing software. Simulation studies demonstrate that this approach yields valid type 1 errors and greater powers than the conditional logistic approach in nested case-control designs across various sample sizes and magnitudes of the hazard ratios. A generalization of the method is also made to incorporate additional matching and the stratified Cox model. The proposed method is illustrated with data from a cohort of children with Wilm's tumor to study the association between histological signatures and relapses.

• Communications for Statistical Applications and Methods
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• 제23권6호
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• pp.555-562
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• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

• Korean Journal of Radiology
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• 제25권2호
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• pp.189-198
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• 2024

Objective: To investigate the prognostic utility of radiomics features extracted from ¹⁸F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent ¹⁸F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.