• Korean Journal of Radiology
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• v.23 no.1
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• pp.139-149
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• 2022

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

• Clinical and Experimental Reproductive Medicine
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• v.36 no.4
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• pp.255-263
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• 2009

Objective: The objective of the study was to determine the diagnostic performance of transabdominal ultrasound by receiver operating characteristic (ROC) curve analysis, in order to evaluate the usefulness in establishing the diagnosis of polycystic ovary syndrome (PCOS). Methods: Questionnaires were given to 8,793 reproductive women reviewed at Ewha Womans University Mokdong hospital. Ultrasound examinations were performed in 701 women with a transabdominal transducer. Transabdominal ultrasounds were performed in 185 normal control women (normal menstruation without hyperandrogenism or PCO morphology) and 248 PCOS patients according to National Institutes of Health (NIH) PCOS diagnosis criteria. ROC curves were calculated for ovarian volume and follicle number. Results: In normal control group, the mean age were $23.64{\pm}4.26$ years old and the mean ovarian volume and follicle number were $6.03{\pm}1.89\;cm^3$ and $6.49{\pm}1.93$, respectively. The ovarian volume showed an area under the ROC curve (AURC) of 0.761. A ovarian volume decision threshold >$9\;cm^3$ had a sensitivity of 51.0% and a specificity of 91.4% for the diagnosis of PCOS. The follicle number showed an AURC of 0.733. A follicle number decision threshold ${\geq}9$ had a sensitivity of 54.9% and a specificity of 87.0% for the diagnosis of PCOS. A follicle number decision threshold ${\geq}10$ had a sensitivity of 53.2% and a specificity of 90.4%. A follicle number and a ovarian volume did not have a high diagnostic power for screening for PCOS. Conclusion: Our results suggest that transabdominal ultrasound assessment is not effective for the detection of PCOS in young women of reproductive age.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.399-408
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• 2022

The purpose of this study was to compare the usefulness of the lipid ratio indicators for the diagnosis of metabolic syndrome in the elderly aged 65 years or older. From January 2018 to December 2020, 1,464 people aged 65 years or older who underwent a health checkup at a general hospital in Seoul were included. Lipid ratio indicators were measured through blood tests. The prevalence of metabolic syndrome according to the quartiles of the lipid ratio index was confirmed by logistic regression analysis. In addition, the metabolic syndrome predictive ability and cutoff value of the lipid ratio indices were estimated with the receiver operating characteristic(ROC) curve. The correlation between atherogenic index of plasma(AIP) and waist circumference was the highest in both men and women(r=0.278, p<0.001 vs r=0.252, p<0.001). As for the lipid ratio indices, the incidence of metabolic syndrome was higher in the fourth quartile than in the first quartile. The area under the ROC curve(AUC) value of AIP was higher at 0.826(95% CI=0.799-0.850) and 0.852(95% CI=0.820-0.881) for men and women, respectively, compared to other lipid ratio indicators, and the optimal cutoff values for both men and women was 0.44(p<0.001). Therefore, the AIP among the lipid ratio indicators was found to be the most useful index for diagnosing metabolic syndrome in the elderly aged 65 years or older.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1173-1181
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• 2020

Recently, as local heavy rains occur frequently in a short period of time, economic and social impacts are increasing beyond the simple primary damage. In advanced meteorologically advanced countries, realistic and reliable impact forecasts are conducted by analyzing socio-economic impacts, not information transmission as simple weather forecasts. In this paper, the degree of flooding was derived using the Spatial Runoff Assessment Tool (S-RAT) and FLO-2D models to calculate the threshold rainfall that can affect human walking, and the threshold rainfall of the concept of Grid to Grid (G2G) was calculated. In addition, although it was used a lot in the medical field in the past, a quantitative accuracy analysis was performed through the ROC analysis technique, which is widely used in natural phenomena such as drought or flood and machine learning. As a result of the analysis, the results of the time period similar to that of the actual and simulated immersion were obtained, and as a result of the ROC (Receiver Operating Characteristic) curve, the adequacy of the fair stage was secured with more than 0.7.

• The Korean Journal of Nuclear Medicine
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• v.22 no.1
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• pp.39-45
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• 1988

To evaluate diagnostic accuracy of liver scintigraphy we analysed liver scans of 143 normal and 258 patients with various liver diseases. Three ROC curves for SOL, liver cirrhosis and diffuse liver disease were fitted using rating methods and areas under the ROC curves and their standard errors were calculated by the trapezoidal rule and the variance of the Wilcoxon statistic suggested by McNeil. We compared these results with that of National Institute of Radiological Science in Japan. 1) The sensitivity of liver scintigraphy was 74.2% in SOL, 71.8% in liver cirrhosis and 34.0% in diffuse liver disease. The specificity was 96.0% in SOL, 94.2% in liver cirrhosis and 87.6% in diffuse liver diasease. 2) ROC curves of SOL and liver cirrhosis approached the upper left-hand corner closer than that of diffuse liver disease. Area (${\pm}$ standard error). under the ROC curve was $0.868{\pm}0.024$ in SOL and $0.867{\pm}0.028$ in liver cirrhosis. These were significantly higher than $0.658{\pm}0.043$ in diffuse liver disease. 3) There was no interobserver difference in terms of ROC curves. But low sensitivty and high specificity of authors' SOL diagnosis suggested we used more strict decision threshold.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.1005-1012
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• 2009

The area under the curve(AUC) is commonly used as a measure of the receiver operating characteristic(ROC) curve which displays the performance of a set of binary classifiers for all feasible ratios of the costs associated with true positive rate(TPR) and false positive rate(FPR). In the bipartite ranking problem where one has to compare two different observations and decide which one is "better", the AUC measures the quantity that ranking score of a randomly chosen sample in one class is larger than that of a randomly chosen sample in the other class and hence, the function which maximizes an AUC of bipartite ranking problem is different to the function which maximizes (minimizes) accuracy (misclassification error rate) of binary classification problem. In this paper, we develop a way to construct the unified framework for AUC maximizer including support vector machines based on maximizing large margin and logistic regression based on estimating posterior probability. Moreover, we develop an efficient algorithm for the proposed unified framework. Numerical results show that the propose unified framework can treat various methodologies successfully.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4091-4094
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• 2014

Background: This study used the receiver operating characteristic curve (ROC) to analyze Surveillance, Epidemiology and End Results (SEER) bronchioaveolar carcinoma data to identify predictive models and potential disparity in outcomes. Materials and Methods: Socio-economic, staging and treatment factors were assessed. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict cause specific survival. The area under the ROC was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of cause specific death was computed for the predictors for comparison. Results: There were 7,309 patients included in this study. The mean follow up time (S.D.) was 24.2 (20) months. Female patients outnumbered male ones 3:2. The mean (S.D.) age was 70.1 (10.6) years. Stage was the most predictive factor of outcome (ROC area of 0.76). After optimization, several strata were fused, with a comparable ROC area of 0.75. There was a 4% additional risk of death associated with lower county family income, African American race, rural residency and lower than 25% county college graduate. Radiotherapy had not been used in 2/3 of patients with stage III disease. Conclusions: There are socio-economic disparities in cause specific survival. Under-use of radiotherapy may have contributed to poor outcome. Improving education, access and rates of radiotherapy use may improve outcome.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.773-786
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• 2014

We propose the multiplication of false rates (MFR) which is a classification accuracy criteria and an area type of rectangle from ROC curve. Optimal threshold obtained using MFR is compared with other criteria in terms of classification performance. Their optimal thresholds for various distribution functions are also found; consequently, some properties and advantages of MFR are discussed by comparing FNR and FPR corresponding to optimal thresholds. Based on general cost function, cost ratios of optimal thresholds are computed using various classification criteria. The cost ratios for cost curves are observed so that the advantages of MFR are explored. Furthermore, the de nition of MFR is extended to multi-dimensional ROC analysis and the relations of classification criteria are also discussed.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.542-544
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• 1998