• Hip & pelvis
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• v.30 no.4
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• pp.219-225
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• 2018

Purpose: Ischiofemoral impingement (IFI)-primarily diagnosed by magnetic resonance imaging (MRI)-is an easily overlooked disease due to its low incidence. The purpose of this study was to evaluate the usefulness of false profile view as a screening test for IFI. Materials and Methods: Fifty-eight patients diagnosed with IFI between June 2013 and July 2017 were enrolled in this retrospective study. A control group (n=58) with matching propensity scores (age, gender, and body mass index) were also included. Ischiofemoral space (IFS) was measured as the shortest distance between the lateral cortex of the ischium and the medial cortex of lesser trochanter in weight bearing hip anteroposterior (AP) view and false profile view. MRI was used to measure IFS and quadratus femoris space (QFS). The receiver operating characteristics (ROC), area under the ROC curve (AUC) and cutoff point of the IFS were measured by false profile images, and the correlation between the IFS and QFS was analyzed using the MRI scans. Results: In the false profile view and hip AP view, patients with IFI had significantly decreased IFS (P<0.01). In the false profile view, ROC AUC (0.967) was higher than in the hip AP view (0.841). Cutoff value for differential diagnosis of IFI in the false profile view was 10.3 mm (sensitivity, 88.2%; specificity, 88.4%). IFS correlated with IFS (r=0.744) QFS (0.740) in MRI and IFS (0.621) in hip AP view (P<0.01). Conclusion: IFS on false profile view can be used as a screening tool for potential IFI.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.48 no.3
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• pp.167-173
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• 2022

Objectives: The purpose of this study was to estimate the minimal clinically important difference (MCID) of mouth opening (MO) and patient satisfaction in surgically treated oral submucous fibrosis (OSMF) patients. Materials and Methods: The status of MO was collected preoperatively (T0), postoperatively at 3 months (T1), and at a minimum of 6 months postoperatively (T2). MCID was determined through the anchor-based approach with the change difference method, mean change method, and receiver operator characteristic curve (ROC) method. Results: In this study, 35 patients enrolled and completed postoperative follow-up (T2) averaging a duration of 18.1 months. At T1, using the change difference method, MO was 14.89 mm and the ROC curve exhibited a 11.5 gain in MO (sensitivity 81.8% and specificity 100%, area under the curve [AUC] of 0.902) and was classified as MCID as reported by patients. At T2, MCID of MO was 9.75 mm using the change difference method and 11.75 mm by the mean change method. The ROC curve revealed that the MCID of MO at T2 was 10.5 mm with 73.9% sensitivity and 83.3% specificity (AUC of 0.873). The kappa value was 0.91, confirming reliability of the data. Conclusion: This study demonstrated MCID values that indicate the clinical relevance of surgical treatment of OSMF if the minimum possible gain in MO is approximately 10 mm.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.6923-6928
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• 2015

Background: Early detection of various kinds of cancers nowadays is needed including colorectal cancer due to the highly significant effects in improving cancer treatment. The aim of this study was to evaluate the potential value of adiponectin, visfatin and resistin as early biomarkers for colorectal cancer patients. Materials and Methods: Serum levels of adiponectin, visfatin and resistin were measured by a sandwich-enzyme-linked (ELISA) assay technique in 114 serum samples comprising 34 patients with colorectal cancer (CRC), 27 with colonic polyps (CP), 24 with inflammatory bowel disease (IBD) and 29 healthy controls. The diagnostic accuracy of each serum marker was evaluated using receiver-operating characteristic (ROC) curve analysis. Results: The mean concentration of adiponectin was significantly higher in CRC and CP groups than IBD and control groups (P-value <0.05). Also the mean concentration of serum resistin was significantly elevated in the IBD and control groups compared to CRC and CP groups (P-value = 0.014). However, no significant difference was noted in patients of the CRC and CP groups. On the other hand, the mean concentration of visfatin was significantly elevated in CRC and control groups compared to CP and IBD groups (P-value = 0.03). ROC analysis curves for the studied markers revealed that between CRC and IBD groups serum level of adiponectin had a sensitivity of 76.7% and a specificity of 76% at a cut off value of 3940, +LR being 3.2 and -LR 0.31 with AUC 0.852, while serum level of adiponectin between CP and IBD had a sensitivity of 77.8% and a specificity of 75% at a cut off value of 3300, with +LR=3.11 and -LR = 0.3 with AUC 0.852. On the other hand the serum level of visfatin between CRC and CP groups had a sensitivity of 65.5% and a specificity of 66.7 at a cut off value of 2.4, +LR being 1.67 and -LR 0.52 with AUC 0.698. Also the serum level of resistin had a sensitivity of 62.5% and a specificity of 70.3% at a cut off value of 24500, with +LR=2.1 and -LR = 0.53 with AUC 0.685 between control and other groups. On the other hand by comparing control vs CP groups resistin had a sensitivity of 81.8% and a specificity of 70.8% at a cut off value of 17700, with +LR=2.8 and -LR = 0.26 with AUC 0.763 while visfatin had a sensitivity of 68.2% and a specificity of 70.8% at a cut off value of 2.7, with +LR=2.34 and -LR = 0.0.45 with AUC 0.812. Conclusions: These findings support potential roles of adiponectin, visfatin and resistin in early detection of CRC and discrimination of different groups of CRC, CP or IBD patients from normal healthy individuals.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.395-407
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• 2020

Most classification accuracy measures for optimal threshold are divided into two types: one is expressed with cumulative distribution functions and probability density functions, the other is based on ROC curve and AUC. Unal (2017) proposed the index of union (IU) as an accuracy measure that considers two types to get them. In this study, ten kinds of accuracy measures (including IU) are divided into six categories, and the advantages of the IU are studied by comparing the measures belonging to each category. The optimal thresholds of these measures are obtained by setting various normal mixture distributions; subsequently, the first and second type of errors as well as the error sums corresponding to each threshold are calculated. The properties and characteristics of the IU statistic are explored by comparing the discriminative power of other accuracy measures based on error values.The values of the first type error and error sum of IU statistic converge to those of the best accuracy measures of the second category as the mean difference between the two distributions increases. Therefore, IU could be an accuracy measure to evaluate the discriminant power of a model.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.707-712
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• 2015

Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.4
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• pp.277-288
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• 2020

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm's ability to interpret seemed to be excellent.

• Imaging Science in Dentistry
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• v.51 no.4
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• pp.363-372
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• 2021

Purpose: This systematic review and meta-analysis assessed the diagnostic accuracy of imaging examinations for the detection of peri-implant bone defects and compared the diagnostic accuracy between titanium (Ti) and zirconium dioxide (ZrO₂) implants. Materials and Methods: Six online databases were searched, and studies were selected based on eligibility criteria. The studies included in the systematic review underwent bias and applicability assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and a random-effect meta-analysis. Summary receiver operating characteristic (sROC) curves were constructed to compare the effect of methodological differences in relation to the variables of each group. Results: The search strategy yielded 719 articles. Titles and abstracts were read and 61 studies were selected for full-text reading. Among them, 24 studies were included in this systematic review. Most included studies had a low risk of bias (QUADAS-2). Cone-beam computed tomography (CBCT) presented sufficient data for quantitative analysis in ZrO₂ and Ti implants. The meta-analysis revealed high levels of inconsistency in the latter group. Regarding sROC curves, the area under the curve (AUC) was larger for the overall Ti group (AUC=0.79) than for the overall ZrO₂ group (AUC=0.69), but without a statistically significant difference between them. In Ti implants, the AUCs for dehiscence defects(0.73) and fenestration defects(0.87) showed a statistically significant difference. Conclusion: The diagnostic accuracy of CBCT imaging in the assessment of peri-implant bone defects was similar between Ti and ZrO₂ implants, and fenestration was more accurately diagnosed than dehiscence in Ti implants.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1305-1315
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• 2015

Consider the risk score which is a function of a linear score for the classification models. The AUC optimization method can be applied to estimate the coefficients of linear score. These estimates obtained by this AUC approach method are shown to be better than the maximum likelihood estimators using logistic models under the general situation which does not fit the logistic assumptions. In this work, the VUS and HUM approach methods are suggested by extending AUC approach method for more realistic discrimination and prediction worlds. Some simulation results are obtained with both various distributions of thresholds and three kinds of link functions such as logit, complementary log-log and modified logit functions. It is found that coefficient prediction results by using the VUS and HUM approach methods for multiple categorical classification are equivalent to or better than those by using logistic models with some link functions.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.829-839
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• 2013

This study is a descriptive research study with the purpose of predicting and comparing factors of depression affecting residents in a metropolitan city by using logistic regression analysis and decision-making tree analysis. The subjects for the study were 462 residents ($20{\leq}aged{\angle}65$) in a metropolitan city. This study collected data between October 7, 2011 and October 21, 2011 and analyzed them with frequency analysis, percentage, the mean and standard deviation, ${\chi}^2$-test, t-test, logistic regression analysis, roc curve, and a decision-making tree by using SPSS 18.0 program. The common predicting variables of depression in community residents were social dysfunction, perceived physical symptom, and family support. The specialty and sensitivity of logistic regression explained 93.8% and 42.5%. The receiver operating characteristic (roc) curve was used to determine an optimal model. The AUC (area under the curve) was .84. Roc curve was found to be statistically significant (p=<.001). The specialty and sensitivity of decision-making tree analysis were 98.3% and 20.8% respectively. As for the whole classification accuracy, the logistic regression explained 82.0% and the decision making tree analysis explained 80.5%. From the results of this study, it is believed that the sensitivity, the classification accuracy, and the logistics regression analysis as shown in a higher degree may be useful materials to establish a depression prediction model for the community residents.

• Journal of Veterinary Clinics
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• v.39 no.5
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• pp.199-206
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• 2022

Postpartum diseases should be predicted to prevent productivity loss before calving especially in organic dairy farms. This study was aimed to investigate the incidence of postpartum metabolic diseases in an organic dairy farm in Korea, to confirm the association between diseases and prepartum blood biochemical parameters, and to evaluate the accuracy of these parameters with a receiver operating characteristic (ROC) analysis for identifying vulnerable cows. Data were collected from 58 Holstein cows (16 primiparous and 42 multiparous) having calved for 2 years on an organic farm. During a transition period from 4 weeks prepartum to 4 weeks postpartum, blood biochemistry was performed through blood collection every 2 weeks with a physical examination. Thirty-one (53.4%) cows (9 primiparous and 22 multiparous) were diagnosed with at least one postpartum disease. Each incidence was 27.6% for subclinical ketosis, 22.4% for subclinical hypocalcemia, 12.1% for retained placenta, 10.3% for displaced abomasum and 5.2% for clinical ketosis. Between at least one disease and no disease, there were significant differences in the prepartum levels of parameters like body condition score (BCS), non-esterified fatty acid (NEFA), total bilirubin (T-bil), direct bilirubin (D-bil) and NEFA to total cholesterol (T-chol) ratio (p < 0.05). The ROC analysis of each of these prepartum parameters had the area under the curve (AUC) <0.7. However, the ROC analysis with logistic regression including all these parameters revealed a higher AUC (0.769), sensitivity (71.0%), and specificity (77.8%). The ROC analysis with logistic regression including the prepartum BCS, NEFA, T-bil, D-bil, and NEFA to T-chol ratio can be used to identify cows that are vulnerable to postpartum diseases with moderate accuracy.